Multiple instance learning: A survey of problem characteristics and applications

Carbonneau, Marc-André; Cheplygina, Veronika; Granger, Eric; Gagnon, Ghyslain

doi:10.1016/j.patcog.2017.10.009

Cited by 740Machine Learning and AlgorithmsMachine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

Multiple instance learning: A survey of problem characteristics and applications

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Carbonneau, Marc-André, Cheplygina, Veronika, Granger, Eric, Gagnon, Ghyslain·2018·Pattern Recognition

DOI 10.1016/j.patcog.2017.10.009

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With over 30 billion sequences in total, this represents one of the largest multiple instance learning experiments ever conducted.
“With over 30 billion sequences in total, this represents one of the largest multiple instance learning experiments ever conducted (Carbonneau et al., 2018).”
From Hopfield Networks is All You Need (2020) · cites this paper as Carbonneau et al., 2018
The Introduction (p. 1) discusses growing data volumes in multiple instance learning, and §6.2 (p. 19) describes a dataset with more than 10 million instances. Neither passage establishes that an experiment involving over 30 billion sequences is among the largest ever conducted. Searches for “30 billion,” “sequences,” and experiment size found no passage supporting that comparison. (LLM, conf=0.76)
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ImageNet Large Scale Visual Recognition Challenge
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Statistical Comparisons of Classifiers over Multiple Data Sets
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Cross validatory choice and assessment of statistical predictions
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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The Earth Mover's Distance as a Metric for Image Retrieval
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Estimating attributes: Analysis and extensions of RELIEF
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“of the Relief [82] feature selection algorithm for MIL.”
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“Even if the Birds and the Newsgroups data sets both possess low WR, it would seem that supervised methods are better suited for this task than MIL methods which use bag accuracy as an optimization objective (MILES, EMDD and MIL Boost).”
Method
Robust object tracking with online multiple instance learning
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“There are external methods like SMOTE [156] and RUSBoost [157] that necessitate accurate labels to perform over or under sampling.”
Method
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777 · cited 1× · see citing works
“All methods in this experiment use an SVM which is known to be vulnerable to label noise [172].”
Method
The pyramid match kernel: discriminative classification with sets of image features
Grauman, K. L., & Darrell, T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.239 · Cited by 1,500 · cited 1× · see citing works
“In BoW models, this can be achieved by dividing the images [78, 79] or videos [75] into different spatial and/or temporal zones.”
Background
Frustratingly Easy Domain AdaptationPreprint
Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,471 · cited 1× · see citing works
“Instance could be mapped to the same semantic space to be compared directly, graph model could be used to aggregate several heterogeneous descriptors or instances could be combined in pairs to create new spaces for comparison similarly to [169].”
Background
Support Vector Machines for Multiple-Instance Learning
Andrews, S. J., Tsochantaridis, I., & Hofmann, T. F. (2002) · Cited by 1,370 · see citing works
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298754 · Cited by 1,305 · see citing works
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“This agrees with other bag-of-words (BoW) empirical comparisons [50, 51].”
Background
A Framework for Multiple-Instance Learning
Maron, O., & Lozano‐Pérez, T. (1997) · Cited by 1,232 · see citing works
Simultaneous Detection and Segmentation
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,176 · cited 1× · see citing works
“Efforts were dedicated to localize objects and segment them at pixel-level using traditional segmentation algorithms such as Constraint Parametric Min-Cuts [110], JSEG [74] or Multi-scale combinatorial grouping [111].”
Method
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)
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“There exist many methods to deal with imbalanced data [155].”
Method
Exploring Strategies for Training Deep Neural Networks
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Sampling Strategies for Bag-of-Features Image Classification
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“This agrees with other bag-of-words (BoW) empirical comparisons [50, 51].”
Background
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
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Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
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Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions
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MILES: Multiple-Instance Learning via Embedded Instance Selection
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“MILES [4]: In Multiple-Instance Learning via Embedded instance Selection (MILES) an SVM classifies bags represented by a feature vectors containing maximal similarities to selected prototypes.”
MethodInfluential
Discriminative learned dictionaries for local image analysis
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587652 · Cited by 722 · cited 1× · see citing works
“Some of these methods learn features in a supervised manner to obtain a more discriminative representation [161], or, in deep learning, a supervised training phase is often used to fine tune the features learned in an unsupervised manner [162].”
Method
Action bank: A high-level representation of activity in video
Sadanand, S., & Corso, J. J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247806 · Cited by 696 · cited 1× · see citing works
“between low-level features and concepts [203, 204, 205].”
Multiple Instance Boosting for Object Detection
Zhang, C., Platt, J. C., & Viola, P. A. (2005) · neural information processing systems · Cited by 692 · see citing works
Controlling the Sensitivity of Support Vector Machines
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Image Categorization by Learning and Reasoning with Regions
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EM-DD: An Improved Multiple-Instance Learning Technique
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Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities
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“Capturing this structure may lead to better classification performance [10, 75, 76].”
Background
Solving Multiple-Instance Problem: A Lazy Learning Approach
Wang, J., Zucker, E. J.-D., & Zucker, J. (2000) · CogPrints (University of Southampton) · Cited by 570 · see citing works
Multiple-Instance Learning for Natural Scene Classification
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Multiple-Instance Active Learning
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Multi-instance multi-label learning
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“This idea has been the object of several publications [37, 38, 39].”
Background
Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420 · cited 1× · see citing works
“Also, several authors noted that in this context, MIL algorithms are sensitive to initialization [9,107].”
Background
Letter Recognition Using Holland-Style Adaptive Classifiers
Frey, P. W., & Slate, D. J. (1991) · Machine Learning · doi:10.1023/a:1022606404104 · Cited by 407 · see citing works
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“To deal with similar instances, miGraph [10] builds a graph per bag and groups similar instances together to adjust their relative importance based on the group size.”
BackgroundMethodInfluential
A review of multi-instance learning assumptions
Foulds, J. R., & Frank, E. (2010) · The Knowledge Engineering Review · doi:10.1017/s026988890999035x · Cited by 397 · see citing works
Deep multiple instance learning for image classification and auto-annotation
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“The weak supervision can come from description sentences [103–105], web search engine results [106], tags associated with similar images and words found on web pages associated with the images [2].”
Background
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005
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Proceedings of International Conference on Acoustics Speech and Signal Processing ICASSP’98
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“For example, the computational complexity of 1915 an SVM is between O(n2) and O(n3) when using traditional QP and LP solvers [180], where n is the number of instances.”
Method
From Group to Individual Labels Using Deep Features
Kotzias, D., Denil, M., de Freitas, N., & Smyth, P. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783380 · Cited by 314 · cited 1× · see citing works
“In [40, 125, 129, 130], MIL was used to infer the sentiment expressed in individual sentences based on the labels provided for entire user reviews.”
Method
Parameterized neural networks for high-energy physics
Baldi, P., Cranmer, K. S., Faucett, T., Sadowski, P., & Whiteson, D. (2016) · The European Physical Journal C · doi:10.1140/epjc/s10052-016-4099-4 · Cited by 285 · see citing works
Weakly supervised histopathology cancer image segmentation and classification
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“The iseases and image modalities used are very diverse; applications nclude classification of cancer in histopathology images [141] , diaetes in retinal images [142] , dementia in brain MR [143] , tubercuosis in X-ray images [144] , classification of a chronic lung disease n CT [145] and others.”
Background
Learning to Extract Relations from the Web using Minimal Supervision
Bunescu, R., & Mooney, R. J. (2007) · Cited by 265 · see citing works
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
Murray, J. F., Hughes, G. F., & Kreutz-Delgado, K. (2005) · doi:10.5555/1046920.1088699 · Cited by 260 · see citing works
Real-time visual tracking via online weighted multiple instance learning
Zhang, K., & Song, H. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.07.013 · Cited by 257 · cited 1× · see citing works
“Also in videos, MIL methods were proposed to perform object tracking [61, 138, 139].”
Method
Multiple-Instance Learning for Medical Image and Video Analysis
Quellec, G., Cazuguel, G., Cochener, B., & Lamard, M. (2017) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2651164 · Cited by 244 · see citing works
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Correlated Label Propagation with Application to Multi-label Learning
Kang, F., Jin, R., & Sukthankar, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.90 · Cited by 231 · cited 1× · see citing works
“Thus, the observation of nature segments might help to decide if the image contains a cocktail or a bear [68].”
Background
Rank-loss support instance machines for MIML instance annotation
Briggs, F., Fern, X. Z., & Raich, R. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339616 · Cited by 223 · cited 4× · see citing works
“From these few data sets, to our knowledge, there is only one (Birds [12]) that supply instance labels and is non-artificial.”
BackgroundMethodInfluential
Supervised versus multiple instance learning
Ray, S., & Craven, M. W. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102439 · Cited by 222 · cited 6× · see citing works
“Ray and Craven [18] compared the performance of MIL methods against supervised methods on MIL problems.”
BackgroundMethodInfluential
Joint multi-label multi-instance learning for image classification
Zha, Z.-J., Hua, X., Mei, T., Wang, J., Qi, G.-J., & Wang, Z. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587384 · Cited by 222 · cited 3× · see citing works
“Efforts were dedicated to localize objects and segment them at pixel-level using traditional segmentation algorithms such as Constraint Parametric Min-Cuts [110], JSEG [74] or Multi-scale combinatorial grouping [111].”
Method
Logistic Regression and Boosting for Labeled Bags of Instances
Xu, X., & Frank, E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24775-3_35 · Cited by 220 · cited 1× · see citing works
“Also, some methods represent bags by the average of the instances they contain, like NSK-SVM [63], or by considering their contribution to the bag label equally [64].”
Method
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.006 · Cited by 213 · see citing works
Simultaneous Detection and SegmentationPreprint
Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.1808 · Cited by 210 · cited 1× · see citing works
“Efforts were dedicated to localize objects and segment them at pixel-level using traditional segmentation algorithms such as Constraint Parametric Min-Cuts [110], JSEG [74] or Multi-scale combinatorial grouping [111].”
Method
On learning to localize objects with minimal supervisionPreprint
Song, H. O., Girshick, R., Jegelka, S., Mairal, J., Harchaoui, Z., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1024 · Cited by 196 · cited 1× · see citing works
“Also, several authors noted that in this context, MIL algorithms are sensitive to initialization [9,107].”
Background
Multiple instance learning for sparse positive bags
Bunescu, R., & Mooney, R. J. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273510 · Cited by 193 · cited 3× · see citing works
“For example, the scenario where the number of positive instances in a bag is low was referred to as either sparse bags [26, 27] or low witness rate [24, 28].”
BackgroundMethod
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
Molinelli, E. J., Korkut, A., Wang, W., Miller, M. L., Gauthier, N. P., Jing, X., Kaushik, P., He, Q., Mills, G. B., Solit, D. B., et al. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003290 · Cited by 191 · see citing works
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
Hauptmann, A. G., Yan, R., Lin, W., Christel, M. G., & Wactlar, H. D. (2007) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.900150 · Cited by 190 · cited 1× · see citing works
“between low-level features and concepts [203, 204, 205].”
MILIS: Multiple Instance Learning with Instance Selection
Fu, Z., Robles‐Kelly, A., & Zhou, J. W. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.155 · Cited by 182 · see citing works
Bag Constrained Structure Pattern Mining for Multi-Graph Classification
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On the relation between multi-instance learning and semi-supervised learning
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“Some methods label all instances independently, like mi-SVM [6] and MissSVM [58].”
Method
A Two-Level Learning Method for Generalized Multi-instance Problems
Weidmann, N., Frank, E., & Pfahringer, B. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39857-8_42 · Cited by 162 · cited 1× · see citing works
“levels of generality for multiple concept assumptions of this type [32].”
Background
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
Yang, C., Dong, M., & Hua, J. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.250 · Cited by 162 · cited 2× · see citing works
“In [57], a different weight is attributed to FP and FN during the optimization of an SVM.”
Background
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning
Vezhnevets, A., & Buhmann, J. M. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540060 · Cited by 161 · see citing works
Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
Stikic, M., Larlus, D., Ebert, S., & Schiele, B. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.36 · Cited by 155 · cited 1× · see citing works
“For instance, in [105, 160] MIL is used to recognize human activities from wearable body sensors.”
Background
JCLEC: a Java framework for evolutionary computation
Ventura, S., Romero, C., Zafra, A., Delgado, J. A. S., & Hervás‐Martínez, C. (2007) · Soft Computing · doi:10.1007/s00500-007-0172-0 · Cited by 153 · cited 1× · see citing works
“There are also machine learning and data mining software packages such as Weka [176], KEEL [177] and JCLEC [178] for which MIL modules exist.”
Method
Solving multi-instance problems with classifier ensemble based on constructive clustering
Zhou, Z., & Zhang, M.-L. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0029-3 · Cited by 150 · see citing works
Multi-instance clustering with applications to multi-instance prediction
Zhang, M.-L., & Zhou, Z. (2008) · Applied Intelligence · doi:10.1007/s10489-007-0111-x · Cited by 144 · cited 2× · see citing works
“k -Medoids and the Hausdorff distance [48]).”
Method
z-SVM: An SVM for Improved Classification of Imbalanced Data
Imam, T., Ting, K. M., & Kamruzzaman, J. (2006) · Lecture notes in computer science · doi:10.1007/11941439_30 · Cited by 142 · see citing works
MIForests: Multiple-Instance Learning with Randomized Trees
Leistner, C., Saffari, A., & Bischof, H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_3 · Cited by 141 · see citing works
Multi-Instance Learning Based Web Mining
Zhou, Z., Jiang, K., & Li, M. (2005) · Applied Intelligence · doi:10.1007/s10489-005-5602-z · Cited by 140 · see citing works
Multiple instance learning with bag dissimilarities
Cheplygina, V., Tax, D. M. J., & Loog, M. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.022 · Cited by 139 · cited 3× · see citing works
“In [69], it is shown that different birds are often heard in the same audio fragment, so a “negative” bird song could help to correctly classify the bird of interest.”
BackgroundMethodSelf-citation
Multiple Instance Regression
Ray, S., & Page, D. (2001) · Cited by 138 · see citing works
Semantic Model Vectors for Complex Video Event Recognition
Merler, M., Huang, B., Xie, L., Hua, G., & Natsev, A. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2168948 · Cited by 137 · cited 1× · see citing works
“Alternatively, events in videos can be represented, among others, by frame, texture and motion descriptors [167, 168].”
Background
Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning
Zhu, J.-Y., Wu, J., Xu, Y. L., Chang, E., & Zhuowen Tu, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2353617 · Cited by 135 · see citing works
Bayesian multiple instance learning
Raykar, V. C., Krishnapuram, B., Bi, J., Dündar, M., & Rao, R. B. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390258 · Cited by 134 · see citing works
A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R. H. H. M., Reither, K., Breuninger, M., Adetifa, I., Maane, R., Ayles, H., & Sá‎nchez, C. I. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2350539 · Cited by 130 · cited 5× · see citing works
“Quantitative evaluation is performed in [58, 142, 144].”
BackgroundInfluential
Video Event Detection by Inferring Temporal Instance Labels
Lai, K.-T., Yu, F. X., Chen⋆, M.-S., & Chang, S. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.288 · Cited by 126 · cited 1× · see citing works
“It has been used to recognize complex events such as “attempting a board trick” or “birthday party” [8, 113].”
Method
MISSL
Rahmani, R., & Goldman, S. A. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143933 · Cited by 125 · cited 1× · see citing works
“Because of these attractive properties, MIL has been increasingly used in many other application fields over the last 20 years, such as image and video classification [4, 5, 6, 7, 8, 9], document 30”
Method
Keywords to visual categories: Multiple-instance learning forweakly supervised object categorization
Vijayanarasimhan, S., & Grauman, K. L. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587632 · Cited by 123 · see citing works
Computer-aided diagnosis from weak supervision: A benchmarking study
Kandemir, M., & Hamprecht, F. A. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.11.010 · Cited by 123 · see citing works
Multiple Instance Learning for Computer Aided Diagnosis
Fung, G., Dündar, M., Krishnapuram, B., & Rao, R. B. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0058 · Cited by 118 · cited 2× · see citing works
“Computational Complexity It has been noted by several authors that many MIL algorithms are too computationally expensive to be used with large data sets [179, 15].”
BackgroundMethod
A Note on Learning from Multiple-Instance Examples
Blum, A. L., & Kalai, A. T. (1998) · Machine Learning · doi:10.1023/a:1007402410823 · Cited by 115 · see citing works
Neural Networks for Multi-Instance Learning
Zhou, Z., & Zhang, M.-L. (2002) · Cited by 114 · see citing works
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
Fuduli, A., Gaudioso, M., & Giallombardo, G. (2004) · SIAM Journal on Optimization · doi:10.1137/s1052623402411459 · Cited by 108 · cited 1× · see citing works
“this problem, in [46], a bundle algorithm [183] is used to solve the SVM optimization problem in linear time (O(n)).”
Method
Boosting for Multi-Graph Classification
Wu, J. X., Pan, S., Xingquan Zhu, A., & Cai, Z. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2327111 · Cited by 108 · cited 1× · see citing works
“In [78, 100], complex objects such as web pages and scientific 680”
Background
Proceedings of the European Conference on Machine Learning (ECML)
Clark, A., Florêncio, C. C., & Watkins, C. (2006) · Cited by 107 · see citing works
Machine Hearing: An Emerging Field [Exploratory DSP
Lyon, R. F. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.937498 · Cited by 107 · cited 1× · see citing works
“Some methods represent audio signals as spectrograms and use image recognition techniques to perform recognition [134].”
Method
Improve Multi-Instance Neural Networks through Feature Selection
Zhang, M.-L., & Zhou, Z. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000016836.03614.9f · Cited by 105 · see citing works
Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)
(2024) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611978032 · Cited by 98 · see citing works
A Convex Method for Locating Regions of Interest with Multi-instance Learning
Li, Y., Kwok, J. T.-Y., Tsang, I. W.-H., & Zhou, Z. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04174-7_2 · Cited by 97 · see citing works
A multiple-instance learning framework for diabetic retinopathy screening
Quellec, G., Lamard, M., Abràmoff, M. D., Decencière, É., Laÿ, B., Erginay, A., Cochener, B., & Cazuguel, G. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.06.003 · Cited by 97 · cited 2× · see citing works
“Quantitative evaluation is performed in [53, 120, 122].”
Background
Multiple Instance Classification via Successive Linear Programming
Mangasarian, O. L., & Wild, E. W. (2007) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-007-9343-5 · Cited by 94 · see citing works
Multiple-Instance Learning For Music Information Retrieval
Mandel, M., & Ellis, D. P. W. (2008) · doi:10.7916/d8086fpd · Cited by 93 · cited 1× · see citing works
“In [133], the objective is to automatically determine the genre of musical excerpts.”
Background
2016 IEEE International Conference on Multimedia and Expo (ICME)
(2016) · doi:10.1109/icme36285.2016 · Cited by 93 · see citing works
Music Emotion Recognition by Multi-label Multi-layer Multi-instance Multi-view Learning
Wu, B., Zhong, E., Horner, A., & Yang, Q. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654904 · Cited by 89 · cited 1× · see citing works
“This is analogous to multi-view MIL which has been studied in a few papers [209, 210, 211, 212].”
Result
Localized content based image retrieval
Rahmani, R., Goldman, S. A., Zhang, H., Krettek, J. E., & Fritts, J. E. (2005) · doi:10.1145/1101826.1101863 · Cited by 88 · cited 3× · see citing works
“In this section, the reference methods with instance classification capabilities will be compared on three benchmark data sets: SIVAL, Birds and Newsgroups.”
Method
Action Recognition with Actons
Zhu, J., Wang, B., Yang, X., Zhang, W., & Tu, Z. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.442 · Cited by 88 · cited 2× · see citing works
“A method based on multiple instance clustering was also proposed to discover a set of actons (sub-actions) from videos to create a mid-level representation of actions [118].”
BackgroundMethod
Deterministic Annealing for Multiple-Instance Learning
Gehler, P., & Chapelle, O. (2007) · Cited by 86 · see citing works
Systematically Differentiating Functions for Alternatively Spliced Isoforms through Integrating RNA-seq Data
Eksi, R., Li, H.-D., Menon, R., Wen, Y., Omenn, G. S., Kretzler, M., & Guan, Y. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003314 · Cited by 86 · cited 1× · see citing works
“MIL has been used, among others, to predict a drug’s bioavailability [42], predict the binding affinity of peptides to major histocompatibility complex molecules [41], discover binding sites governing gene expression [96, 97] and predict gene functions [98].”
Method
Multiple instance learning for soft bags via top instances
Li, W., & Vasconcelos, N. M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299056 · Cited by 86 · cited 2× · see citing works
“For example, in computer vision applications, it is difficult to guarantee that negative images contain no positive patches: An image showing a house may contain flowers, but is unlikely to be annotated as a flower image [92].”
BackgroundMethod
Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning ?
Babenko, B., Zhuowen Tu, A., & Belongie, S. (2008) · Cited by 86 · see citing works
Combining the Right Features for Complex Event Recognition
Tang, K., Yao, B., Fei-Fei, L., & Koller, D. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.335 · Cited by 83 · cited 1× · see citing works
“Alternatively, events in videos can be represented, among others, by frame, texture and motion descriptors [167, 168].”
Background
A Boosting Approach to Multiple Instance Learning
Auer, P. L., & Ortner, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30115-8_9 · Cited by 78 · see citing works
MILD: Multiple-Instance Learning via Disambiguation
Li, W.-J., & Yeung, D. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.58 · Cited by 77 · see citing works
MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets
Bandyopadhyay, S., Ghosh, D., Mitra, R., & Zhao, Z. (2015) · Scientific Reports · doi:10.1038/srep08004 · Cited by 74 · cited 1× · see citing works
“affinity of peptides to major histocompatibility complex molecules [45], discover binding sites governing gene expression [119, 120] and predict gene functions [121].”
Background
Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis
Pappas, N., & Popescu-Belis, A. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1052 · Cited by 74 · cited 2× · see citing works
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
BackgroundMethod
Detector discovery in the wild: Joint multiple instance and representation learning
Hoffman, J., Pathak, D., Darrell, T., & Saenko, K. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298906 · Cited by 73 · cited 1× · see citing works
“In several methods for object localization, bags are composed of many candidate bounding boxes corresponding to instances [1, 59, 130, 131, 132].”
Method
Fast Bundle Algorithm for Multiple-Instance Learning
Bergeron, C., Moore, G., Zaretzki, J. M., Breneman, C. M., & Bennett, K. P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.194 · Cited by 73 · cited 2× · see citing works
“Ranking can be performed at the bag level [42] or at the instance level [43].”
BackgroundMethod
A theoretical and empirical analysis of support vector machine methods for multiple-instance classification
Doran, G. B., & Ray, S. (2013) · Machine Learning · doi:10.1007/s10994-013-5429-5 · Cited by 72 · see citing works
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
Jorgensen, Z., Zhou, Y., & Inge, M. (2008) · Cited by 69 · see citing works
Multiple-instance ranking: Learning to rank images for image retrieval
Hu, Y., Li, M., & Yu, N. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587352 · Cited by 69 · cited 1× · see citing works
“Ranking can be performed at the bag level [42] or at the instance level [43].”
Background
Multiple instance learning of real valued data
Dooly, D. R., Zhang, Q., Goldman, S. A., & Amar, R. (2003) · doi:10.5555/944919.944949 · Cited by 68 · see citing works
Scalable Multi-instance Learning
Wei, X.-S., Wu, J., & Zhou, Z. (2014) · Proceedings · doi:10.1109/icdm.2014.16 · Cited by 67 · cited 1× · see citing works
“Many of these methods, like BoW [60, 70], miFV [62], FAMER [71] or PPMM [72] represent bags as instance distributions which indirectly account for co-occurrence.”
Method
Multi-modal image annotation with multi-instance multi-label LDA
Nguyen, C.-T., Zhan, D., & Zhou, Z. (2013) · Cited by 67 · see citing works
Learning from Positive and Unlabeled Examples: A Survey
Zhang, B., & Zuo, W. (2008) · doi:10.1109/isip.2008.79 · Cited by 66 · cited 1× · see citing works
“This type of problem is well studied in single-instance learning [188], 2010”
Background
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs
Kandemir, M., Zhang, C., & Hamprecht, F. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10470-6_29 · Cited by 65 · see citing works
Positive and Unlabeled Multi-Graph Learning
Wu, J. X., Pan, S., Xingquan Zhu, A., Zhang, C., & Wu, X. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2527239 · Cited by 65 · see citing works
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)
Chandler, J., & Richard, B. P. (2016) · Cited by 61 · see citing works
Robust multiple-instance learning ensembles using random subspace instance selection
Carbonneau, M.-A., Granger, E., Raymond, A. J., & Gagnon, G. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.035 · Cited by 60 · see citing works
Self-citation
Learning Discriminative Space–Time Action Parts from Weakly Labelled Videos
Sapienza, M., Fabio Cuzzolin, A., & Torr, P. H. S. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0662-8 · Cited by 58 · cited 1× · see citing works
“In several methods for object localization, bags are composed of many candidate bounding boxes corresponding to instances [1, 59, 130, 131, 132].”
Method
On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R. H. H. M., Ayles, H., & Sá‎nchez, C. I. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2505672 · Cited by 58 · see citing works
Sparse coding and classifier ensemble based multi-instance learning for image categorization
Xiang-fa, S., Jiao, L., Yang, S., Zhang, X., & Shang, F. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.07.029 · Cited by 56 · see citing works
Interactive localized content based image retrieval with multiple-instance active learning
Zhang, D., Wang, F., Shi, Z., & Zhang, C. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.03.002 · Cited by 55 · cited 1× · see citing works
“Existing literature on this subject is rather limited [149, 197, 198, 199].”
Background
Adaptive p-posterior mixture-model kernels for multiple instance learning
Wang, H., Yang, Q., & Zha, H. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390299 · Cited by 55 · cited 2× · see citing works
“Many of these methods, like BoW [70, 94], miFV [72], FAMER [95] or PPMM [96] represent bags as instance distribu645”
Method
Concurrent Multiple Instance Learning for Image Categorization
Qi, G.-J., Hua, X., Rui, Y., Mei, T., Tang, J., & Zhang, H.-J. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383152 · Cited by 49 · cited 1× · see citing works
“This has also been directly modeled in a tensor model [73] and in a multi-label framework [74].”
Method
Classification of COPD with Multiple Instance Learning
Cheplygina, V., Sorensen, L., Tax, D. M. J., Pedersen, J. H., Loog, M., & de Bruijne, M. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.268 · Cited by 48 · see citing works
Self-citation
Convex Multiple-Instance Learning by Estimating Likelihood Ratio
Li, F., & Sminchisescu, C. (2010) · Cited by 47 · see citing works
Multiple-instance learning as a classifier combining problem
Li, Y., Tax, D. M. J., Duin, R. P. W., & Loog, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.08.018 · Cited by 45 · see citing works
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Zhang, C., Chen, X., Chen, M., Chen, S., & Shyu, M. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521628 · Cited by 45 · see citing works
Multiple instance ranking
Bergeron, C., Zaretzki, J. M., Breneman, C. M., & Bennett, K. P. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390163 · Cited by 44 · cited 1× · see citing works
“Thus, many methods using SVM and SVM-like algorithms [4, 6, 27, 179, 181, 182] rapidly become impractical as the number of instances increases [46].”
Method
A Multi-scale Multiple Instance Video Description NetworkPreprint
Xu, H., Venugopalan, S., Ramanishka, V., Rohrbach, M., & Saenko, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05914 · Cited by 43 · see citing works
Multi-instance Multi-graph Dual Embedding Learning
Wu, J. X., Xingquan Zhu, A., Zhang, C., & Cai, Z. (2013) · Proceedings · doi:10.1109/icdm.2013.121 · Cited by 40 · cited 1× · see citing works
“This is analogous to multi-view MIL which has been studied in a few papers [209, 210, 211, 212].”
Result
Learning from multi-topic web documents for contextual advertisement
Zhang, Y., Surendran, A. C., Platt, J. C., & Narasimhan, M. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1402015 · Cited by 40 · cited 3× · see citing works
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
BackgroundMethod
An empirical study on image bag generators for multi-instance learning
Wei, X.-S., & Zhou, Z. (2016) · Machine Learning · doi:10.1007/s10994-016-5560-1 · Cited by 36 · cited 3× · see citing works
“• When it is necessary to model combinations of instances to infer bag labels, bag-level and embedding methods perform better [15, 21, 49].”
BackgroundInfluential
Multiple Instance Learning on Structured Data
Zhang, D., Liu, Y., Si, L., Zhang, J., & Lawrence, R. D. (2011) · Cited by 36 · see citing works
Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure
Bi, J., & Liang, J. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383141 · Cited by 36 · see citing works
Efficient multi-instance learning for activity recognition from time series data using an auto-regressive hidden Markov model
Guan, X., Raich, R., & Wong, W. (2016) · International Conference on Machine Learning · Cited by 36 · see citing works
Single- vs. multiple-instance classification
Alpaydın, E., Cheplygina, V., Loog, M., & Tax, D. M. J. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.006 · Cited by 35 · see citing works
Self-citation
ReliefF-MI: An extension of ReliefF to multiple instance learning
Zafra, A., Pechenizkiy, M., & Ventura, S. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.03.052 · Cited by 35 · see citing works
Confidence-Rated Multiple Instance Boosting for Object Detection
Ali, K., & Saenko, K. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.312 · Cited by 34 · cited 1× · see citing works
“Other methods, like CR-MILBoost [66] and RSIS [30], estimate the probability that each instance is positive before training an ensemble of classifiers.”
Method
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering
Zhang, D., Wang, F., Si, L., & Li, T. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2109011 · Cited by 33 · see citing works
Horror video scene recognition via Multiple-Instance learning
Wang, J., Li, B., Hu, W., & Wu, O. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946656 · Cited by 33 · cited 1× · see citing works
“This problem formulation is also used in [114] to recognize scenes that are inappropriate for children.”
Background
Instance-level accuracy versus bag-level accuracy in multi-instance learning
Vanwinckelen, G., Tragante, V., Daan Fierens, A., & Blockeel, H. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0416-z · Cited by 33 · cited 9× · see citing works
“• The performance of MIL algorithms depends on several properties of the data set [15,18,20,21,23,53] .”
BackgroundMethodInfluential
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.94 · Cited by 32 · cited 2× · see citing works
“Alternatively, on can simply replace a bag-level classifier by a regressor [45].”
Background
Label Stability in Multiple Instance Learning
Cheplygina, V., Sørensen, L., Tax, D. M. J., de Bruijne, M., & Loog, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_66 · Cited by 32 · cited 1× · see citing works
“However, challenges and conclusions related to problem characteristics are also applicable to the other tasks.”
BackgroundSelf-citation
On classification with bags, groups and sets
Cheplygina, V., Tax, D. M. J., & Loog, M. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.03.008 · Cited by 31 · see citing works
Self-citation
Feature Learning from Spectrograms for Assessment of Personality Traits
Carbonneau, M., Granger, É., Attabi, Y., & Gagnon, G. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2763132 · Cited by 31 · cited 1× · see citing works
“In [136], personality traits are inferred from speech signals represented as spectrograms in a BoW framework.”
Self-citation
G3P-MI: A genetic programming algorithm for multiple instance learning
Zafra, A., & Ventura, S. (2010) · Information Sciences · doi:10.1016/j.ins.2010.07.031 · Cited by 30 · cited 2× · see citing works
“Finally, parallelization can be employed to reduce computation time, like in [187], where a parallelized version of the G3P-MI [83] algorithm have been proposed to leverage the power of GPUs, and thus deal with large quantities of 1965”
Method
Aerosol Optical Depth Prediction from Satellite Observations by Multiple Instance Regression
Wang, Z., Radosavljević, V., Han, B., Zoran Obradovic, A., & Vučetić, S. (2008) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972788.15 · Cited by 30 · cited 1× · see citing works
“In this survey, the collective assumption designates all assumptions in which more than one instance defines bag labels.”
Background
MI2LS
Zhang, D., He, J., & Lawrence, R. D. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487651 · Cited by 29 · cited 1× · see citing works
“Alternatively, instances can be sentences [40,125], passages [6,126] or paragraphs [18].”
Background
Instance-level semisupervised multiple instance learning
Jia, Y., & Zhang, C. (2008) · Cited by 29 · see citing works
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
McGovern, A., & Jensen, D. D. (2003) · doi:10.21236/ada465314 · Cited by 27 · cited 4× · see citing works
“This is an important topic that has been addressed in some BoW methods, but was never thoroughly studied in other types of MIL methods, except for some methods using graphs [10, 26, 77, 78, 99].”
BackgroundMethodInfluential
Salience Assignment for Multiple-Instance Regression
Wagstaff, K. L., & Lane, T. D. R. (2007) · Cited by 26 · see citing works
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Zafra, A., Pechenizkiy, M., & Ventura, S. (2011) · Information Sciences · doi:10.1016/j.ins.2011.01.034 · Cited by 26 · see citing works
Ensemble one-class support vector machines for content-based image retrieval
Wu, R.-S., & Chung, W.-H. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.05.037 · Cited by 25 · see citing works
Locating Regions of Interest in CBIR with Multi-instance Learning Techniques
Zhou, Z., Xue, X., & Jiang, Y. Y. (2005) · Lecture notes in computer science · doi:10.1007/11589990_12 · Cited by 24 · cited 2× · see citing works
“adapted in [66] to perform instance classification.”
Background
Speeding up multiple instance learning classification rules on GPUs
Cano, A., Zafra, A., & Ventura, S. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-014-0752-0 · Cited by 23 · cited 1× · see citing works
“Finally, parallelization can be employed to reduce computation time, like in [187], where a parallelized version of the G3P-MI [83] algorithm have been proposed to leverage the power of GPUs, and thus deal with large quantities of 1965”
Method
The Detection of Concept Frames Using Clustering Multi-instance Learning
Tax, D. M. J., Hendriks, E. A., Valstar, M., & Pantić, M. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.715 · Cited by 23 · cited 3× · see citing works
“Spatial dependency in images have also been modeled in with CRF in [74, 82].”
Method
A co-training framework for visual tracking with multiple instance learning
Lu, H., Zhou, Q.-H., Wang, D., & Xiang, R. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771455 · Cited by 22 · cited 1× · see citing works
“Also in videos, MIL methods were proposed to perform object tracking [61, 138, 139].”
Method
Multi-Instance Learning with Any Hypothesis ClassPreprint
Sabato, S., & Tishby, N. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.2021 · Cited by 21 · cited 2× · see citing works
“Sabato and Tishby [47] analyzed the of sample complexity in MIL, and they found that the statistical performance of MIL is only mildly dependent on the number of instances per bag.”
Background
Multi-Instance Dimensionality Reduction
Sun, Y., Ng, M. K., & Zhou, Z. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7700 · Cited by 20 · see citing works
Deep Multi-Instance Transfer LearningPreprint
Kotzias, D., Denil, M., Blunsom, P., & de Freitas, N. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.3128 · Cited by 19 · cited 1× · see citing works
“In [40, 125, 129, 130], MIL was used to infer the sentiment expressed in individual sentences based on the labels provided for entire user reviews.”
Method
A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification
Wang, Q., Si, L., & Zhang, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_47 · Cited by 17 · cited 1× · see citing works
“The mixturemodel in [110] naturally represents different positive clusters.”
Background
Multiple instance learning-based birdsong classification using unsupervised recording segmentation
Ruiz-Muñoz, J. F., Orozco‐Alzate, M., & Castellanos-Dominguez, G. (2015) · International Conference on Artificial Intelligence · Cited by 17 · see citing works
Weakly supervised scalable audio content analysis
Kumar, A., & Raj, B. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552989 · Cited by 17 · see citing works
Multiple-instance learning from distributions
Doran, G. B., & Ray, S. (2016) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 17 · see citing works
Maximum margin multiple-instance feature weighting
Chai, J., Chen, H., Huang, L., & Shang, F. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2013.12.009 · Cited by 15 · cited 3× · see citing works
“This type of data is often difficult to handle by classifiers using Euclidean-like distance measures.”
BackgroundMethodInfluential
Multiple instance learning for landmine detection using ground penetrating radar
Manandhar, A., Morton, K. D., Collins, L. M., & Torrione, P. A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.917947 · Cited by 15 · cited 1× · see citing works
“In [140,141], MIL classifiers detect buried landmines from ground-penetrating radar signals.”
Background
A multiple instance learning approach for landmine detection using Ground Penetrating Radar
Karem, A., & Frigui, H. (2011) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2011.6049271 · Cited by 15 · cited 1× · see citing works
“In [140,141], MIL classifiers detect buried landmines from ground-penetrating radar signals.”
Background
Progressive Learning for Interactive Surveillance Scenes Retrieval
Meessen, J., Desurmont, X., Delaigle, J.-F., Vleeschouwer, C. D., & Macq, B. M. M. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383517 · Cited by 15 · cited 1× · see citing works
“Existing literature on this subject is rather limited [149, 197, 198, 199].”
Background
Learning Curves for the Analysis of Multiple Instance Classifiers
Tax, D. M. J., & Duin, R. P. W. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_76 · Cited by 15 · cited 1× · see citing works
“In APR [3] the region is a hyper-rectangle, while in many others it is one, or a collection of, hyper-spheres/-ellipses [29, 31, 33, 88].”
Background
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning
Venkatesan, R., Chandakkar, P. S., & Li, B. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.299 · Cited by 14 · cited 3× · see citing works
“[52] showed that simple lazy-learning techniques could be applied to some MIL problems to obtain results comparable to state-of-the-art techniques.”
BackgroundResultInfluential
Avoiding False Positive in Multi-Instance Learning
Han, Y., Tao, Q., & Wang, J. (2012) · Cited by 14 · see citing works
Characterizing Multiple Instance Datasets
Cheplygina, V., & Tax, D. M. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24261-3_2 · Cited by 14 · cited 2× · see citing works
“• The performance of MIL algorithms depends on several properties of the data set [15, 18, 20, 21, 23, 48].”
BackgroundSelf-citation
Multiple-Instance Learning with Instance Selection via Dominant Sets
Erdem, A., & Erdem, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24471-1_13 · Cited by 13 · cited 1× · see citing works
“In [94], robustness to label noise is obtained by using dominant sets to perform clustering and select relevant instance prototype in a bag-embedding algorithm similar to MILES [4].”
Method
Multimedia Event Detection Using Event-Driven Multiple Instance Learning
Phan, S., Le, D.-D., & Satoh, S. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806330 · Cited by 13 · cited 2× · see citing works
“Because of these attractive properties, MIL has been increasingly used in many other application fields over the last 20 years, such as image and video classification [4, 5, 6, 7, 8, 9], document 30”
Method
Multi-Instance Learning from Positive and Unlabeled Bags
Wu, J. X., Xingquan Zhu, A., Zhang, C., & Cai, Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06608-0_20 · Cited by 13 · cited 1× · see citing works
“So far, only a handful of papers are dedicated to this subject [189, 190, 191].”
Background
Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace
Ping, W., Xu, Y., Ren, K., Chi, C., & Shen, F. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7653 · Cited by 12 · see citing works
Multiple instance learning based on positive instance selection and bag structure construction
Li, Z., Geng, G., Feng, J. H., Peng, J., Wen, C., & Liang, J. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.11.013 · Cited by 11 · see citing works

Flagged first, then by citations

2 of the 223 references couldn’t be matched to a known paper.

Research works printed but not linked: 8

[5][5] R. Rahmani and S. A. Goldman, 'MISSL: Multiple-instance Semi-supervised Learning,' in ICML , 2006. -
[6][6] S. Andrews, I. Tsochantaridis, and T. Hofmann, 'Support Vector Machines for MultipleInstance Learning,' in NIPS , 2002. -
[46][46] F. Herrera, S. Ventura, R. Bello, C. Cornelis, A. Zafra, D. S´ anchez-Tarrag´ o, and S. Vluymans, Multiple Instance Learning: Foundation and Algorithms . Springer, 2016. -
[51][51] H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid, 'Evaluation of Local SpatioTemporal Features for Action Recognition,' in BMVC , 2009. -
[115][115] B. Babenko, M.-H. Yang, and S. Belongie, 'Robust Object Tracking with Online Multiple Instance Learning,' IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, no. 8, pp. 1619-1632, Aug. 2011. -
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Sorted by citations

Attention-based Deep Multiple Instance LearningPreprint
Ilse, M., Tomczak, J. M., & Welling, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04712 · Cited by 1,235 · see citing works
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
Li, B., Li, Y., & Eliceiri, K. W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01409 · Cited by 957 · see citing works
“We refer the readers to [4] for a survey.”
Background
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
Zhong, J.-X., Li, N., Kong, W., Liu, S., Li, T. H., & Li, G. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00133 · Cited by 626 · see citing works
“…that the label Y = 0 is noiseless, since it means all snippets v i in the video V are normal: However, Y = 1 is noisy because in this case the video V is partially made up of anomalous clips: This is referred to as one-sided label noise [7, 9, 55], for the noise only appears along with Y = 1 .”
Background
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
Transformers in medical image analysis
He, K., Chen, G., Li, Z., Rekik, I., Yin, Z., Wen, J., Gao, Y., Wang, Q., Zhang, J., & Shen, D. G. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.07.002 · Cited by 571 · see citing works
Federated Learning and Differential Privacy for Medical Image Analysis
Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. (2021) · doi:10.21203/rs.3.rs-1005694/v1 · Cited by 464 · see citing works
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
Vanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. D. A. B., Geneslaw, L., Rizvi, H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00416-8 · Cited by 395 · see citing works
Multiple instance learning for histopathological breast cancer image classification
Sudharshan, P. J., Petitjean, C., Spanhol, F., Oliveira, L. E., Heutte, L., & Honeine, P. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.049 · Cited by 369 · see citing works
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning
Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X. L., Wei, H., & Zhang, W. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996256 · Cited by 323 · see citing works
“are beneficial for MIL classifier [23].”
A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
Mahadevkar, S. V., Khemani, B., Patil, S., Kotecha, K. V., Vora, D. R., Abraham, A., & Gabralla, L. A. (2022) · IEEE Access · doi:10.1109/access.2022.3209825 · Cited by 304 · see citing works
“In training bags, image segmentation and feature extraction are performed, based on the size of dictionary bag features computed and finally applied, a classifier; the model predicts the result [30].”
Method
Hopfield Networks is All You Need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv · doi:10.48550/arxiv.2008.02217 · Cited by 236 · see citing works
“With over 30 billion sequences in total, this represents one of the largest multiple instance learning experiments ever conducted [15] (for details see [70], appendix, Section A2).”
Background
Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study
Nirthika, R., Manivannan, S., Ramanan, A., & Wang, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06953-8 · Cited by 219 · see citing works
“…are differential approximation of max pooling, they are widely explored in (non-CNN based) Multiple Instance Learning approaches in computer vision [8] and medical image analysis [76, 135] (Table 2), in addition to CNN-based image classiﬁcation [28, 42, 124, 144] and segmentation [90, 129].”
Method
Weakly supervised machine learning
Ren, Z., Wang‎, S., & Zhang, Y. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12216 · Cited by 174 · see citing works
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, C. W., Cheng, K.-T. T., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3357877 · Cited by 174 · see citing works
“In breast cancer imaging, the most used weakly-supervised learning methods are class activation map (CAM) [61] and multiple instancelearning(MIL)[62].”
Towards Safe Weakly Supervised Learning
Li, Y.-F., Guo, L.-Z., & Zhou, Z.-H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2922396 · Cited by 157 · see citing works
“There aremanystudies [4], [5], [6], [7], [8],”
BackgroundMethodInfluential
Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks
Wang, L., Zhang, L., Zhu, M., Qi, X., & Zhang, Y. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101665 · Cited by 150 · see citing works
Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential
Gadermayr, M., & Tschuchnig, M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102337 · Cited by 161 · see citing works
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Luo, Z., Guillory, D., Shi, B., Ke, W., Wan, F., Darrell, T., & Xu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_43 · Cited by 140 · see citing works
Saliency detection for strip steel surface defects using multiple constraints and improved texture features
Song, G., Song, K., & Yan, Y. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.106000 · Cited by 140 · see citing works
Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients
Tsai, P.-C., Lee, T.-H., Kuo, K.-C., Su, F.-Y., Lee, T.-L. M., Marostica, E., Ugai, T., Zhao, M., Lau, M. C., Väyrynen, J. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37179-4 · Cited by 136 · see citing works
“Moreover, MOMA revealed the previously unknown correlation between histopathology image patterns and the expression levels of BECN1 (Supplemental Figure 2A), with the results validated in the NHS-HPFS dataset (Supplemental Figure 2B and Supplemental Table 1).”
A multi-resolution model for histopathology image classification and localization with multiple instance learning
Li, J., Li, W., Sisk, A. E. J., Ye, H., Wallace, W. D., Speier, W. F., & Arnold, C. W. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104253 · Cited by 118 · see citing works
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationPreprint
Widrich, M., Schäfl, B., Pavlović, M., Ramsauer, H., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G. K., Greiff, V., Hochreiter, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.12.038158 · Cited by 102 · see citing works
“However, in MIL problems considered by machine learning methods up to now, the number of instances per bag is in the range of hundreds or few thousands (Carbonneau et al., 2018; Lee et al., 2019) (see also Tab.”
A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis
Jiang, P., Li, X., Shen, H., Chen, Y., Wang, L., Chen, H., Feng, J., & Liu, J. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10588-z · Cited by 111 · see citing works
Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue
Ostmeyer, J., Christley, S., Toby, I. T., & Cowell, L. G. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2292 · Cited by 98 · see citing works
“Mapping pertinent receptors in a repertoire to a single phenotype label can formally be described as MIL, which treats problems as bags of instances where the bags are labeled but the instances are not (24).”
Background
Representation Learning of Histopathology Images using Graph Neural Networks
Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109 · see citing works
“[3].”
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Khirodkar, R., Chari, V., Agrawal, A., & Tyagi, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00311 · Cited by 95 · see citing works
“Our multi-instance pose network is fully supervised and not related to multiple instance learning [4, 46], which is a form of weakly-supervised learning paradigm where training instances are arranged in sets.”
Background
Deep Learning-Based Action Detection in Untrimmed Videos: A Survey
Vahdani, E., & Tian, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3193611 · Cited by 93 · see citing works
“are arranged in sets, called bags, and a label is provided for the entire bag [67].”
Background
Deep computational pathology in breast cancer
Duggento, A., Conti, A., Mauriello, A., Guerrisi, M., & Toschi, N. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.08.006 · Cited by 88 · see citing works
“The authors employed (for the first time in computational pathology) a learning method termed Multiple Instance Learning (MIL) (see [117] for a review).”
Method
Artificial intelligence applications in prostate cancer
Baydoun, A., Jia, A. Y., Zaorsky, N. G., Kashani, R., Rao, S. K., Shoag, J. E., Vince, R., Bittencourt, L. K., Zuhour, R. J., Price, A. T., et al. (2023) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-023-00684-0 · Cited by 84 · see citing works
Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening
Wang, J., Ji, J., Zhang, M., Lin, J., Zhang, G., Gong, W., Cen, L., Lu, Y., Huang, X., Huang, D., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.8758 · Cited by 86 · see citing works
Weakly Supervised Object Detection in Artworks
Gonthier, N., Gousseau, Y., Ladjal, S., & Bonfait, O. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_53 · Cited by 83 · see citing works
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
Hong, F.-T., Huang, X., Li, W., & Zheng, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_21 · Cited by 79 · see citing works
Exploring Multiple Instance Learning (MIL): A brief survey
Waqas, M., Ahmed, S. U., Tahir, M. A., Wu, J., & Qureshi, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123893 · Cited by 79 · see citing works
H^2-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis
Hou, W., Yu, L., Lin, C., Huang, H., Yu, R., Qin, J., & Wang, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19976 · Cited by 78 · see citing works
“Readers can refer Carbonneau et al. (2018) for a comprehensive survey.”
Background
Label-efficient learning in agriculture: A comprehensive review
Li, J., Chen, D., Qi, X., Li, Z., Huang, Y., Morris, D., & Tan, X. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108412 · Cited by 82 · see citing works
Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning
Papadopoulos, A., Kyritsis, K., Klingelhoefer, L., Bostanjopoulou, S., Chaudhuri, K. R., & Delopoulos, A. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2961748 · Cited by 79 · see citing works
“Multiple-Instance Learning [15] is a supervised learning scenario, where multiple instances or data points are grouped together to form sets, commonly referred to in MIL terminology as bags [16].”
A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection
Choudhry, N., Abawajy, J., Huda, S., & Rao, I. (2023) · IEEE Access · doi:10.1109/access.2023.3321800 · Cited by 83 · see citing works
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.009 · Cited by 93 · see citing works
Self-citation
Detection of Breast Cancer From Whole Slide Histopathological Images Using Deep Multiple Instance CNN
Das, K., Conjeti, S., Chatterjee, J., & Sheet, D. (2020) · IEEE Access · doi:10.1109/access.2020.3040106 · Cited by 73 · see citing works
“The conventional MIL implementations were used from [49].”
Method
Melanoma Detection by Means of Multiple Instance Learning
Astorino, A., Fuduli, A., Veltri, P., & Vocaturo, E. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00341-y · Cited by 70 · see citing works
Appropriateness of performance indices for imbalanced data classification: An analysis
Mullick, S. S., Datta, S., Dhekane, S. G., & Das, S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107197 · Cited by 70 · see citing works
“A natural future extension of this work would be to investigate the behavior of indices which are used in imbalanced multi-label [41] and multi-instance [42] classiﬁcation problems.”
Background
Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images
Yu, J.-G., Wu, Z., Ming, Y., Deng, S., Li, Y., Ou, C., He, C., Wang, B., Zhang, P., & Wang, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102748 · Cited by 69 · see citing works
Robust support vector machine with generalized quantile loss for classification and regression
Yang, L., & Dong, H. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105483 · Cited by 67 · see citing works
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations
Marini, N., Marchesin, S., Otálora, S., Wodziński, M., Caputo, A., van Rijthoven, M., Aswolinskiy, W., Bokhorst, J., Podareanu, D., Petters, E., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00635-4 · Cited by 66 · see citing works
Deep Learning in Diverse Intelligent Sensor Based Systems
Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 67 · see citing works
Weakly supervised histopathology image segmentation with self-attention
Li, K., Qian, Z., Han, Y., Chang, E., Wei, B., Lai, M., Liao, J., Fan, Y., & Xu, Y. L. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102791 · Cited by 69 · see citing works
Inductive Relation Prediction by BERT
Zha, H., Chen, Z., & Yan, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20537 · Cited by 64 · see citing works
Multiple instance-based video anomaly detection using deep temporal encoding–decoding
Kamoona, A. M., Gostar, A. K., Bab‐Hadiashar, A., & Hoseinnezhad, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119079 · Cited by 61 · see citing works
Weakly Supervised Action Selection Learning in Video
Ma, J., Gorti, S. K., Volkovs, M., & Yu, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00750 · Cited by 63 · see citing works
“Multiple instance learning [5] is commonly used to train the classifier, where top-k pooling is applied over CAS for each class to aggregate the highest”
Deep learning in histopathology: A review
Banerji, S., & Mitra, S. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1439 · Cited by 63 · see citing works
“A good survey on the state-of-the-art MIL algorithms, their characteristics and challenges, along with several applications, is provided in Carbonneau et al. (2018).”
Background
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Rony, J., Belharbi, S., Dolz, J., Ayed, I. B., McCaffrey, L. M., & Granger, É. (2023) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2023-5g54 · Cited by 72 · see citing works
“Under this scenario, powerful techniques for multiple-instance learning (MIL) (Carbonneau et al., 2018; Cheplygina et al., 2019; Wang et al., 2018; Zhou, 2004) are generally considered, where individual instance labels (e.g . image pixels, segments or patches) are not observable or do not belong to…”
Method
Temporal Cue Guided Video Highlight Detection with Low-Rank Audio-Visual Fusion
Ye, Q., Shen, X., Gao, Y., Wang, Z., Bi, Q., Li, P., & Yang, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00785 · Cited by 60 · see citing works
“Early MIL related works [2, 4] pointed out that simply applying the video label to segment label might be inaccurate since labels in positive video could be noisy.”
Background
Visualizing the outcome of dynamic analysis of Android malware with VizMal
Lorenzo, A. D., Martinelli, F., Medvet, E., Mercaldo, F., & Santone, A. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102423 · Cited by 59 · see citing works
“Another approach which can fit our scenario is the Multiple Instance Learning (MIL) framework [22, 70].”
Background
Digital mapping of heavy metals in urban soils: A review and research challenges
Shi, T., Li, H., Wang, R., Li, Z., Hu, Z., & Wu, G. (2023) · CATENA · doi:10.1016/j.catena.2023.107183 · Cited by 58 · see citing works
Artificial intelligence in COPD CT images: identification, staging, and quantitation
Wu, Y., Xia, S., Liang, Z., Chen, R., & Qi, S. (2024) · Respiratory Research · doi:10.1186/s12931-024-02913-z · Cited by 58 · see citing works
“MIL is a weakly supervised learning paradigm where the training data consists of labeled bags, each containing multiple instances [55].”
Background
Multiple Instance Learning for Emotion Recognition Using Physiological Signals
Romeo, L., Cavallo, A., Pepa, L., Bianchi‐Berthouze, N., & Pontil, M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2954118 · Cited by 58 · see citing works
“MIL has attracted much attention from the research community, especially in recent years, as the amount of data needed to learn difficult tasks has increased exponentially [26].”
Background
Learning to select cuts for efficient mixed-integer programming
Huang, Z., Wang, K., Liu, F., Zhen, H., Zhang, W., Yuan, M., Hao, J., Yu, Y. H., & Wang, Y.-X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108353 · Cited by 56 · see citing works
Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography
Li, X., Guo, R., Lu, J., Chen, T., & Qian, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3236162 · Cited by 56 · see citing works
“The MIL has been widely employed in many computer vision tasks [15], where this framework generally exhibited good feature representation capabilities [12], [16], [17], [18].”
Method
MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data
Guan, H., Wang, C., & Tao, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118586 · Cited by 55 · see citing works
“set contains multiple unlabelled instances [1, 9].”
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Myronenko, A., Xu, Z., Yang, D., Roth, H. R., & Xu, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_32 · Cited by 55 · see citing works
Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring
Tanoni, G., Principi, E., & Squartini, S. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3191908 · Cited by 55 · see citing works
“Learning from bags raises important challenges that are unique to MIL formulation [43].”
Background
AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images
Liu, P., Ji, L., Ye, F., & Fu, B. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103020 · Cited by 55 · see citing works
Machine learning and related approaches in transcriptomics
Cheng, Y., Xu, S.-M., Santucci, K., Lindner, G., & Janitz, M. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.150225 · Cited by 55 · see citing works
Masked Conditional Neural Networks for sound classification
Medhat, F., Chesmore, D., & Robinson, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106073 · Cited by 54 · see citing works
“The evaluation of the MCLNN for sound classification follows the Multiple Instance Learning paradigm [56] used in most of the attempts referenced in the literature, where the original tag of the sound file is used to label each segment in isolation and the final decision for the clip follows a…”
Method
DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis
(2023) · doi:10.1126/sciadv.abo5128 · Cited by 53 · see citing works
“The repertoire-level analysis task can be formulated as a typical MIL problem ( 55 ), where each repertoire can be regarded as a bag containing receptors that are instances.”
Deep multiple-instance learning for abnormal cell detection in cervical histopathology images
Pal, A., Xue, Z., Desai, K. T., Banjo, A. A. F., Adepiti, C. A., Long, L. R., SCHIFFMAN, M. H., & Antani, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104890 · Cited by 53 · see citing works
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning
Rymarczyk, D., Borowa, A., Tabor, J., & Zieliński, B. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00176 · Cited by 58 · see citing works
“MIL problems are common in medical image analysis due to the vast resolution of images and the weakly-labeled small datasets [4, 21].”
Background
Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
Papadopoulos, A., Iakovakis, D., Klingelhoefer, L., Bostantjopoulou, S., Chaudhuri, К. R., Kyritsis, K., Hadjidimitriou, S., Charisis, V. S., Hadjileontiadis, L. J., & Delopoulos, A. N. (2020) · Scientific Reports · doi:10.1038/s41598-020-78418-8 · Cited by 52 · see citing works
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data
Mann, P., Paes, A., & Matsushima, E. H. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7313 · Cited by 53 · see citing works
Real-time incident prediction for online service systems
Zhao, N., Chen, J., Wang, Z., Peng, X., Wang, G., Wu, Y., Zhou, F., Feng, Z., Nie, X., Zhang, W., et al. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409672 · Cited by 52 · see citing works
“To reduce the influence of noisy alerts, eWarn adopts multi-instance learning (MIL) [11].”
BackgroundMethodInfluential
WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images
Silva-Rodríguez, J., Colomer, A., & Naranjo, V. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101846 · Cited by 51 · see citing works
“A recent extensive review of MIL and its characteristics can be found in [6].”
Background
Discrepant multiple instance learning for weakly supervised object detection
Gao, W., Wan, F., Yue, J., Xu, S., & Ye, Q. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108233 · Cited by 51 · see citing works
A Comprehensive Review on Multiple Instance Learning
Fatima, S., Ali, S., & Kim, H. (2023) · Electronics · doi:10.3390/electronics12204323 · Cited by 51 · see citing works
Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening
Chikontwe, P., Luna, M., Kang, M., Hong, K. S., Ahn, J. H., & Park, S. H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102105 · Cited by 51 · see citing works
Decouple and Resolve: Transformer-Based Models for Online Anomaly Detection From Weakly Labeled Videos
Liu, T., Zhang, C., Lam, K., & Kong, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3216479 · Cited by 50 · see citing works
“Wan et al. [23] leverage a center loss to cluster normal instances, which is complementary to the conventional MIL principle for tackling the WS-VAD task.”
BackgroundInfluential
Multi-instance learning of graph neural networks for aqueous p K a prediction
Xiong, J., Li, Z. S., Wang, G., Fu, Z., Zhong, F., Xu, T., Liu, X., Huang, Z., Liu, X., Chen, K., et al. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab714 · Cited by 50 · see citing works
Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context
Stračina, T., Ronzhina, M., Ředina, R., & Novàkovâ, M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.867033 · Cited by 48 · see citing works
“MIL is a type of supervised learning, which works with labeled bags of data (instead of the labeled instances) (Carbonneau et al., 2018).”
Background
DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images
Xu, C., Qi, S., Feng, J., Xia, S., Kang, Y., Yao, Y., & Qian, W. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab857d · Cited by 46 · see citing works
“Second, as one method of MIL, Citation-KNN also has the advantages similar to the image retrieval and ensemble learning (Carbonneau et al 2016).”
BackgroundMethod
A survey of class-imbalanced semi-supervised learning
Gui, Q., Zhou, H., Guo, N., & Niu, B.-N. (2023) · Machine-mediated learning · doi:10.1007/s10994-023-06344-7 · Cited by 48 · see citing works
“…incomplete supervision (Chapelle et al., 2006) (e.g., semi-supervised learning), inexact supervision (Dietterich et al., 1997; Foulds & Frank, 2010; Carbonneau et al., 2018) (e.g.,multi-instance learning) and inaccurate supervision (Frénay & Ver-leysen, 2014; Gao et al., 2016) (e.g., label noise…”
Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark
Bollis, E., Pedrini, H., & Avila, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00043 · Cited by 44 · see citing works
“The labels are provided only for the bags and the instances inherit from the bags creating a weakly supervised environment [4].”
Background
Developing image analysis pipelines of whole-slide images: Pre- and post-processing
Smith, B. H., Hermsen, M., Lesser, E. R., Ravichandar, D., & Kremers, W. K. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.531 · Cited by 43 · see citing works
“More details of these methods are provided in Carbonneau et al [33].”
BackgroundMethod
Dual Attention Networks for Few-Shot Fine-Grained Recognition
Xu, S.-L., Zhang, F., Wei, X.-S., & Wang, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20196 · Cited by 42 · see citing works
“In particular, the MIL methods for image recognition (Tang et al. 2018; Carbonneau et al. 2018; Angles et al. 2021) has also achieved some good results.”
Method
A reinforced active learning approach for optimal sampling in aspect term extraction for sentiment analysis
Venugopalan, M., & Gupta, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118228 · Cited by 42 · see citing works
A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels
Xiao, Y., Liang, F., & Liu, B. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2973450 · Cited by 41 · see citing works
Weakly Supervised Learning for Land Cover Mapping of Satellite Image Time Series via Attention-Based CNN
Ienco, D., Gbodjo, Y. J. E., Gaetano, R., & Interdonato, R. (2020) · IEEE Access · doi:10.1109/access.2020.3024133 · Cited by 41 · see citing works
“Multiple Instance learning [26] (MIL) is a supervised learning paradigm in which a classiﬁcation model is learnt to supply prediction for a group of instances.”
Background
Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging
Zhang, W., Yang, C., Cao, Z., Li, Z., Zhuo, L., Tan, Y., He, Y., Yao, L., Zhou, Q. F., Gong, Q., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104541 · Cited by 41 · see citing works
Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change
Datcu, O., Macovei, C., & Hobincu, R. (2020) · Applied Sciences · doi:10.3390/app10020451 · Cited by 40 · see citing works
“A Monte Carlo analysis [28] with initial conditions x0, y0, z0 chosen randomly from an uniform distribution in [−2,2] was performed.”
Method
Superpixel-based multi-scale multi-instance learning for hyperspectral image classification
Huang, S., Liu, Z., Jin, W., & Mu, Y. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110257 · Cited by 38 · see citing works
Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
Tajeddin, N. Z., Jahanifar, M., Bilal, M., Eastwood, M., & Rajpoot, N. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103071 · Cited by 38 · see citing works
Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems
Carbonneau, M., Granger, É., & Gagnon, G. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869164 · Cited by 38 · see citing works
“Moreover, experiments in [13] and [22] show that single-instance classifiers often perform comparably to MIL methods, especially for instance classification.”
BackgroundMethodInfluentialSelf-citation
Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio
Xu, X., Nemati, E., Vatanparvar, K., Nathan, V., Ahmed, T., Rahman, M. M., McCaffrey, D., Kuang, J., & Gao, J. A. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448124 · Cited by 38 · see citing works
“Labels are only provided for entire sets, and the specific labels of the instances in the bags are not available [16].”
Background
Robust bag classification approach for multi-instance learning via subspace fuzzy clustering
Waqas, M., Tahir, M. A., & Khan, S. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119113 · Cited by 37 · see citing works
A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image
Shi, J., Tang, L., Li, Y., Zhang, X., Gao, Z., Zheng, Y., Wang, C., Gong, T., & Li, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3273236 · Cited by 37 · see citing works
“WSI classification tasks [11], [12], [13], [15], [16], [17], [18], [19], [20], [21], there still exist two limitations for directly applying MIL to the challenging pT staging task.”
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Selçuk, Ş. Y., Yang, X., Bai, B., Zhang, Y., Li, Y., Aydın, M., Unal, A. F., Gomatam, A., Guo, Z., Angus, D. M., et al. (2024) · BME Frontiers · doi:10.34133/bmef.0048 · Cited by 37 · see citing works
“MIL is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag—usually on WSI level— instead of individual instances/image patches [25].”
A Lagrangian Relaxation Approach for Binary Multiple Instance Classification
Astorino, A., Fuduli, A., & Gaudioso, M. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2885852 · Cited by 36 · see citing works
“Exhaustive surveys on MIL problems are [2] and [9].”
Background
A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning
Avolio, M., & Fuduli, A. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015442 · Cited by 36 · see citing works
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
Zankov, D. V., Matveieva, M., Nikonenko, A., Ирекович, Н. Р., Baskin, I. I., Varnek, A. A., Полищук, П. Г., & Madzhidov, T. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00692 · Cited by 36 · see citing works
Weakly-supervised anomaly detection in video surveillance via graph convolutional label noise cleaning
Li, N., Zhong, J.-X., Shu, X.-J., & Guo, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.026 · Cited by 38 · see citing works
Classification in the multiple instance learning framework via spherical separation
Gaudioso, M., Giallombardo, G., Miglionico, G., & Vocaturo, E. (2019) · Soft Computing · doi:10.1007/s00500-019-04255-1 · Cited by 35 · see citing works

Survey of Multi Instance learning Algorithms
Kavitha, M., & Thomas, J. (2018) · IJARCCE · doi:10.17148/ijarcce.2018.7811
Multiple instance learning under real-world conditions
Carbonneau, M. (2017) · Espace ÉTS (ETS) · Cited by 1 · see citing works
Self-citation
Multiple-Instance Learning from Pairwise Comparison Bags
Zhou, S., Shu, S., Wang, H., Wei, H., Xiang, T., & Li, B. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3696460
Multiple-Instance Learning from Similar and Dissimilar Bags
Feng, L., Shu, S.-L., Cao, Y.-Z., Tao, L., Wei, H., Xiang, T., An, B., & Niu, G. (2021) · Knowledge Discovery and Data Mining · doi:10.1145/3447548.3467318 · Cited by 13 · see citing works
A Comprehensive Review on Multiple Instance Learning
Fatima, S., Ali, S., & Kim, H. (2023) · Electronics · doi:10.3390/electronics12204323 · Cited by 51 · see citing works

Authors: Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G.
Affiliations: École de Technologie Supérieure (CA)
Erasmus MC (NL)
Corresponding author: Carbonneau, Marc-André
ORCID: Carbonneau, Marc-André
Cheplygina, Veronika
Gagnon, Ghyslain
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2018-05
Volume: 77
Pages: 329-353
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2017.10.009
OpenAlex: W2560886373
Cited by: 740
References: 223
Citations by year: 2026: 75 · 2025: 118 · 2024: 91 · 2023: 124 · 2022: 98 · 2021: 92 · 2020: 84 · 2019: 41 · 2018: 14 · 2017: 2 · 2015: 1
FWCI: 22.10
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Pattern Recognition (publishedVersion)
arXiv (Cornell University) (submittedVersion)
TU/e Research Portal (submittedVersion)
EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Espace ÉTS (ETS) (submittedVersion)
TU/e Research Portal (submittedVersion, other-oa)
RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: multiple instance learning, weakly supervised learning, problem characteristics, data distribution, label ambiguity, computer vision, document classification

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