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Multiple instance learning: A survey of problem characteristics and applications
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“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
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Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · cited 2×
“There are external methods like SMOTE [193] and RUSBoost [194] that necessitate accurate labels to perform over or under sampling.”
MethodGreedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · cited 1×
“The method is essentially the same as gradient boosting [143] except that the loss function is based on bag classification error.”
MethodThe pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · cited 1×
“Recently, methods for representation learning [160] have gained in popularity because they usually yield a high level of accuracy.”
MethodStatistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881 · cited 1×
“The experiments were conducted using a nested cross-fold validation protocol [149].”
MethodBeyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · cited 1×
“In BoW models for computer vision, this can be achieved by dividing the images [102, 103] or videos [80] into different spatial and/or temporal zones.”
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547
Research works printed but not linked: 1
- Babenko, 2011, Robust object tracking with online multiple instance learning, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1619, 10.1109/TPAMI.2010.226 · DOI · Paper page
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Multiple Instance Learning with Trainable Soft Decision Tree Ensembles
Konstantinov, A. V., Utkin, L. V., & Muliukha, V. (2023) · Algorithms · doi:10.3390/a16080358 · Cited by 4
Moghadam, P. A., Suderman, J. D., Nakhli, R., Rich, K., Asadi, M., Kung, S. H. Y., Oo, H., Keyes, M., Farahani, H., MacAulay, C. E., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_74 · Cited by 4
Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning
Gardner, W., Winkler, D. A., Bamford, S. E., Muir, B. W., & Pigram, P. J. (2024) · Small Methods · doi:10.1002/smtd.202301230 · Cited by 4
Anatomy-Aware Gating Network for Explainable Alzheimer’s Disease Diagnosis
Jiang, H., & Miao, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72086-4_9 · Cited by 4
Dai, H., Li, W., Wang, Q., & Cheng, B. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3509533 · Cited by 4
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding
Wang, H., Ju, C., Lin, W., Ma, C., Xiao, S., Zhang, Y., & Wang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888314 · Cited by 4
“Thanks to the essence of multi-instance learning [8], with well-alignment representations, the event detector can output refined grounding pseudo-labels.”
Group-wise relation mining for weakly-supervised fine-grained multimodal emotion recognition
Jin, X., Wang, J., Qin, H., Ning, X., Xin, T., & Lin, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107543 · Cited by 4
Malware Detection in Docker Containers: An Image is Worth a Thousand Logs
Nousias, A., Katsaros, E., Syrmos, E., Grammatikis, P. I. R., Λάγκας, Θ., Argyriou, V., Moscholios, I. D., Markakis, E., Goudos, S. K., & Sarigiannidis, P. G. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161263 · Cited by 10
“Our contributions are summarized as follows: • Task Formulation: We define the novel task of identifying malware-compromised dockerized software containers with machine learning-based methods and propose a streaming, patch-based CNN approach motivated by Multiple Instance Learning (MIL) [4].”
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Yasuda, M., Harada, N., Ohishi, Y., Saito, S., Nakayama, A., & Ono, N. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3779057 · Cited by 4
“Instead, the event detector can be indirectly trained by multiple instance learning [55] that minimize the binary cross-entropy between the marginalized outputs of event detector ˆ p bag over time and weak labels y bag , where, the weak label y bag is defined as”
Multiple Instance Learning approaches for Melanoma and Dysplastic Nevi images classification
Vocaturo, E., & Zumpano, E. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00217 · Cited by 4
“This design problem is referred as the so-called MIL standard assumption, consisting in the following: a bag is positive if it contains at least a positive instance and it is negative if it does not contain any positive instance [26], [27].”
BackgroundSafe Weakly Supervised Learning
Li, Y.-F. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/701 · Cited by 4
“Multi-instance learning may be outperformed by the naive learning methods which simply assign the coarse-grained label to a bag of instances [Carbonneau et al. , 2018].”
MethodExplainable Multiple Instance Learning with Instance Selection Randomized Trees
Komárek, T., Brabec, J., & Somol, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86520-7_44 · Cited by 4
“Surveys on MIL [1, 6] categorize classification methods into two major groups according to the level at which they operate.”
BackgroundYou, X., Zhang, L., Yu, H., Yuan, M., & Li, X. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3494957 · Cited by 4
“To tackle temporal uncertainty of self-reported labels in HAR, previous solutions based on machine learning usually follow a multi-instance learning (MIL) paradigm [53][8].”
MethodA theoretical analysis based on causal inference and single-instance learning
Wang, C., Lu, X., & Wang, W. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03193-0 · Cited by 4
“In addition, many researchers also try to resolve the problem of distribution change in MIL from different perspectives [15, 29].”
BackgroundPredicting Packaging Sizes Using Machine Learning
Heininger, M., & Ortner, R. (2022) · Operations Research Forum · doi:10.1007/s43069-022-00157-5 · Cited by 4
“As the structure of our problem is different from classical multiple-instance problems, an application of respective learning approaches available in the literature (cf. [ 1] for an overview) seems not promising.”
BackgroundVertebral Compression Fracture detection using Multiple Instance Learning and Majority Voting
Iyer, S., Blair, A., White, C., Dawes, L. C., Moses, D. A., & Sowmya, A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956309 · Cited by 4
A Regional Multiple Instance Learning Network for Whole Slide Image Segmentation
Cai, H., Yi, W., Li, Y., Liao, W., & Song, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995017 · Cited by 4
“Many MIL methods are to train an instance classifier first and choose the highest score instance as a positive instance [32].”
MethodUsing Multiple Instance Learning to Build Multimodal Representations
Wang, P., Wells, W. M., Berkowitz, S., Horng, S., & Golland, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_35 · Cited by 4
“Multiple instance learning is a type of weakly supervised learning that is effective for problems that lack fine-grain annotations [3].”
BackgroundInfluentialLi, F., Wang, M., Bin, H., Duan, X., Zhang, Z., Ye, Z., & Huang, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43904-9_7 · Cited by 5
Huang, Y., Sun, Y., Lai, C., Xu, Q., Wang, X., Shen, X., & Ge, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01489 · Cited by 4
“Given an image pair from ImageNet, our core idea is to follow multiple instance learning (MIL [10, 2, 5, 43, 40]) pipelines to treat images as bags and point-to-point matching pairs as instances to train binary matching classifiers.”
MethodInfluentialAn Experimental Comparison of Different Object Tracking Algorithms
Raghava, N., Gupta, K., Kedia, I., & Goyal, A. (2020) · doi:10.1109/iccsp48568.2020.9182101 · Cited by 4
“With large datasets being used to solve complex issues today, research and interest in multiple instance learning has grown rapidly [6].”
BackgroundState Sequence Pooling Training of Acoustic Models for Keyword Spotting
Łopatka, K., & Bocklet, T. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2722 · Cited by 4
“The setup takes advantage of multiple instance learning which is known to be beneficial for signal classification [21].”
BackgroundLi, X., Wei, B., Li, T., & Zhang, N. (2020) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icast51195.2020.9319486 · Cited by 4
“The MIL task can be divided into the bag-level paradigm and instance-level paradigm, according to how to use the information of multiple instances [15].”
BackgroundScreening for Depressed Individuals by Using Multimodal Social Media Data
Mann, P., Paes, A., & Matsushima, E. H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.17858 · Cited by 4
“First, we argue that the nature of social media posts make them to be better formulated as a Multiple Instance Learning (MIL) task, which works on a weakly supervised learning regime (Carbonneau et al. 2018).”
BackgroundMethodOptimal Representative Distribution Margin Machine for Multi-Instance Learning
Luan, T., Luo, T., Zhuge, W., & Hou, C. (2020) · IEEE Access · doi:10.1109/access.2020.2988764 · Cited by 4
Gupta, V., Pétursson, P., Hilgendorf, L., Rawshani, A., Borén, J., Råmunddal, T., Ömerovic, E., Louca, A., Angerås, O., Schneiderman, J. F., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf029 · Cited by 4
Encrypted Malicious Traffic Detection Using Multi-instance Learning
Zhang, Z., Yin, J., Zhao, L., Chen, J., Du, M., Zhang, Z., & Liu, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97629-2_19 · Cited by 3
Random features meet MIL: a deep GP approach to colorectal MSI prediction
Shen, S., Wang, Z., Liu, T., Ma, K., Tian, Z., Zhang, F., & Zhang, Q. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02214-9 · Cited by 3
“This scenarioistypicalinmultiple-instancelearning(MIL),whichhasbecomea focalpointincancerpredictiontasks 13,14 .”
Zhang, W., Chotcomwongse, P., Li, Y., Xu, P., Yao, R., Zhou, L., Zhou, Y., Feng, H., Zhou, Q., Wang, X., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103942 · Cited by 5
Zhao, W., Peng, X., Wang, X., Qi, J., Yang, Y., & Yang, P. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356289 · Cited by 3
DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Rafsani, F., Shah, J., Chong, C. D., Schwedt, T. J., & Wu, T. S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00375 · Cited by 3
Alibrahim, M. N., Gloghini, A., & Carbone, A. (2026) · Blood Reviews · doi:10.1016/j.blre.2026.101390 · Cited by 3
Multi-instance Learning for Semantic Image Analysis
Tian, D., & Zhang, Y. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-03948-5_38 · Cited by 3
Maximum-margin polyhedral separation for binary Multiple Instance Learning
Astorino, A., Avolio, M., & Fuduli, A. (2023) · EURO Journal on Computational Optimization · doi:10.1016/j.ejco.2023.100070 · Cited by 3
Joint task semi-supervised semantic segmentation for TRUS image
Gao, C., Shi, Y., Zhou, C., Lei, B., & Mukondiwa, D. T. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105654 · Cited by 3
Yin, J., Liu, X., & Yang, Z. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101883 · Cited by 4
“These methods work well when witness rate (WR) (Cheplygina and Tax, 2015; Carbonneau et al., 2018) is extremely low, but work poorly if WR is high, where WR is the proportion of positive instances in positive bags.”
Chen, Y., Liu, X., Fang, H., Zheng, W., Zhou, X., Li, M., Wu, Y., & Wang, C. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18536-x · Cited by 3
Cracking the code of adaptive immunity: The role of computational tools
Vegesana, K., & Thomas, P. G. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.009 · Cited by 3
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance
Akdag, E., Bondarev, E., & de With, P. H. N. (2024) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip63160.2024.10849819 · Cited by 3
Wang, R., Gu, Y., & Yang, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107599 · Cited by 3
Berloco, F., Zaccaria, G. M., Altini, N., Colucci, S., & Bevilacqua, V. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102526 · Cited by 3
Meneghetti, A. R., Ligero, M., Kühn, J., Löck, S., Carrero, Z. I., Pérez-López, R., Bressem, K. K., Brinker, T. J., Pearson, A. T., Truhn, D., et al. (2025) · BMC Artificial Intelligence · doi:10.1186/s44398-025-00003-8 · Cited by 3
“Some of the current FMs require aggregation methods to analyze CT scans.”
Sun, Y., Sun, R., Lv, J., Kong, Q.-X., Dai, C., Wang, B., Han, X., Chen, M., Liu, R., Jiang, Y., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70513 · Cited by 4
Hellström, T. A. E., Ewals, L. J. S., Nederend, J., Luyer, M. D. P., de With, P. H. N., & van der Sommen, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110780 · Cited by 4
Exploiting structural constraints for visual object tracking
Bouachir, W., & Bilodeau, G.-A. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.09.002 · Cited by 3
A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning
Møllersen, K., Hardeberg, J. Y., & Godtliebsen, F. (2020) · Data · doi:10.3390/data5020056 · Cited by 3
Isoform-Disease Association Prediction by Data Fusion
Huang, Q., Wang, J., Zhang, X., & Yu, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57821-3_5 · Cited by 3
“To bypass this issue, some researchers take a gene as a bag and its spliced isoforms as instances of that bag, and adapt multiple instance learning (MIL) [2,17] to distribute the readily available functional annotations of a gene to its isoforms [3,6,14,26,34,40].”
BackgroundWeber, T., Ferrin, M., Sweeney, F., Ahmed, S., & Sharmin, M. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.0-212 · Cited by 3
Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity
Feng, L., Shu, S., Cao, Y., Tao, L., Wei, H., Xiang, T., An, B., & Niu, G. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3232141 · Cited by 3
“This paper considers another weakly supervised learning problem called multiple-instance learning (MIL) [26], [27], [28], [29], [30], which aims to deal with the binary classification task where each training example is represented by a bag of instances and a binary label (indicating whether the bag…”
BackgroundSheng, B., Li, C., Bao, L., & Li, R. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1618 · Cited by 3
“Learning from set-level labels or summary statistics has been seen in the literature such as multipleinstance learning (Carbonneau et al., 2018; Zhou, 2018), aggregate output learning (Musicant, Christensen and Olson, 2007) and Bayesian large-scale multiple regression with summary statistics (Zhu…”
BackgroundSegmentation Pseudolabel Generation Using the Multiple Instance Learning Choquet Integral
McCurley, C. H., & Zare, A. (2023) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2023.3338049 · Cited by 3
“MIL[51]fitstheWSSSproblemperfectly,wherethetaskisto learninstance-level(pixel)classificationlabelsfrombag-level (image)groundtruthinformation.”
Pleasure, M., Redekop, E., Polson, J., Zhang, H., Kaneko, N., Speier, W. F., & Arnold, C. W. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00282 · Cited by 3
“Another limitation of MIL models, in general, is the assumption that patches are independent and identically distributed [19, 11].”
BackgroundBai, Q., Gao, Y., Chen, F., Zhang, Y., Yang, Y., Zhong, L., Yang, W., & Yang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3346910 · Cited by 3
“One representative approach is multiple-instance learning (MIL) [10], which has been widely adopted in classification tasks for CT volume.”
MethodGreenScreen: A Multimodal Dataset for Detecting Corporate Greenwashing in the Wild
Sharma, U., Rudinac, S., Demmers, J., van Dolen, W., & Worring, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56435-2_8 · Cited by 3
MA-MIL: Sampling point-level abnormal ECG location method via weakly supervised learning
Liu, J., Li, J., Duan, Y., Zhou, Y., Fan, X., Li, S., & Chang, S. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108164 · Cited by 3
Weakly supervised object detection for automatic tooth-marked tongue recognition
Zhang, Y., Xu, J., He, Y., Li, S., Luo, Z., & Lei, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107766 · Cited by 3
Action Selection Learning for Weakly Labeled Multi-modal Multi-view Action Recognition
Nguyễn, T. T., Kawanishi, Y., John, V., Komamizu, T., & Ide, I. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3744742 · Cited by 3
“The Frame-level classifier aims to predict video classes by aggregating the top-𝑘 frame probabilities using the Multiple Instance Learning (MIL) [6] method.”
Guo, L.-Z., Kuang, F., Liu, Z.-X., Li, Y.-F., Ma, N., & Qie, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5823 · Cited by 3
Importance Reweighting for Biquality Learning
Nodet, P., Lemaire, V., Bondu, A., Cornuéjols, A., & Ouorou, A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533349 · Cited by 4
“Inappropriate labels: for instance, in Multi Instance Learning (MIL) [7] the labels are assigned to bags of examples, with positive label indicating that at least one example of the bag is positive.”
BackgroundCrowdDetective: Wisdom of the Crowds for Detecting Abnormalities in Medical Scans
Cheplygina, V. (2020) · Journal of Trial and Error · doi:10.36850/rga1 · Cited by 3
“The strategies I will develop are general, therefore I will use them both with traditional [40, 41] and more recent [39, 42] MIL classifiers.”
MethodSelf-citationVizMal: A Visualization Tool for Analyzing the Behavior of Android Malware
Bacci, A., Martinelli, F., Medvet, E., & Mercaldo, F. (2018) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006665005170525 · Cited by 3
“Internally, VizMal analyzes the execution trace using a Multiple Instance Learning (MIL) framework.”
BackgroundA Linear Primal-Dual Multi-Instance SVM for Big Data Classifications
Brand, L., Baker, L. Z., Ellefsen, C., Sargent, J., & Wang, H. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00012 · Cited by 3
A bag-to-class divergence approach to multiple-instance learningPreprint
Møllersen, K., Hardeberg, J. Y., & Godtliebsen, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02782 · Cited by 3
“[11] assume underlying instance labels.”
BackgroundA Multiclass Multiple Instance Learning Method with Exact LikelihoodPreprint
Li, X.-L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12346 · Cited by 3
“Among them, our method is most closely related to MIL (Carbonneau et al., 2018; Zhang & Zhou, 2014) since it also assumes a binary label for each class.”
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled GraphsPreprint
Ji, C., Wang, R., Zhu, R., Cai, Y., & Wu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04333 · Cited by 3
“Compared with traditional supervised learning, weakly supervised learning aims to address the situation in which precise or sufficient labels are unachievable [24, 25].”
BackgroundAnaya, J., Sidhom, J.-W., Mahmood, F., & Baras, A. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.05.237206 · Cited by 3
“Since then multiple instance learning (MIL) has been applied to a range of tasks (Carbonneau et al., 2018), including problems in bioinformatics (Eksi et al.”
MethodSelf-supervised learning for early detection of neurodegenerative diseases with small data
Jiang, H. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/166402 · Cited by 3
Neo-epitope identification by weakly-supervised peptide-TCR binding predictionPreprint
Gao, Y., Gao, Y., Li, W., Wu, S., Xing, F., Zhou, C., Fu, S., Chuai, G., Chen, Q., Zhang, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.02.550128 · Cited by 3
“Y$ = { +1, ∃x$: y$ = +1 −1, ∀x$: y$ = −1 (19)”
Multiple instance learning with noisy labels based on symmetry loss
Zhang, X., Xu, Y., & Liu, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112795 · Cited by 2
Multi-instance curriculum learning for histopathology image classification with bias reduction
Mi, Z., Zhang, J., Liu, X., Yue, G., Yue, J., Wei, M., Li, Y., & Wu, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103647 · Cited by 2
Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification
Gao, Y., Gao, Y., Wu, S., Li, D., Zhou, C., Meng, F., Dong, K., Zhao, X., Li, P., Liang, A., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101403 · Cited by 7
Cui, F.-Q., Lin, Z., Rao, X., Tong, A., Li, S., Wang, F., Chen, C., & Liu, B. (2025) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa67752.2025.00083 · Cited by 2
“Multi-Instance Learning (MIL) [33] is a technique designed to address problems with imprecise labels and has been widely applied in fields such as image processing and sentiment analysis.”
Keshvarikhojasteh, H., Aubreville, M., Bertram, C., Pluim, J. P. W., & Veta, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337261 · Cited by 2
Han, S., Huang, M.-F., Li, Y. Q., Wellbrock, G. A., Xia, T. J., Kotria, S., Mundt, J. A., Moore, J. M., Ji, P. N., Li, T., et al. (2025) · Journal of Lightwave Technology · doi:10.1109/jlt.2025.3649631 · Cited by 2
“This setting can be addressed using standard supervised learning frameworks [34].”
Elhajjajy, S. I., & Weng, Z. (2026) · RNA · doi:10.1261/rna.080892.125 · Cited by 1
“…to formulate the RBP binding prediction task as a multiple instance learning (MIL) learning problem (Dietterich et al. 1997; Herrera et al. 2016; Carbonneau et al. 2018), in which we modeled each sequence in our data set as a “ bag ” and the sequence ’ s constituent contexts as the bag ’ s “…”
Gardner, W., Borg, J., Mouchemore, K. A., Chi, L. H., Bell, C., Nickless, D. K., Yeo, B. J., Bamford, S. E., Winkler, D. A., Muir, B. W., et al. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c06470 · Cited by 2
Interpretable Word-Level Context-Based Sentiment Analysis
Yang, C., Larson, E., & Cao, J. (2026) · Journal of Data Science · doi:10.6339/26-jds1225 · Cited by 2
Multi-instance positive and unlabeled learning with bi-level embedding
Tang, X., Xu, C., Luo, T., & Hou, C. (2022) · Intelligent Data Analysis · doi:10.3233/ida-215896 · Cited by 2
“Since the MIL methods show excellent performance when dealing with problems of insufficient labeling, it is utilized to solve problems in many fields, such as text recognition [4], image classification [1], molecular activity prediction [9] and so on.”
Analyzing Immunomes using Sequence Embedding and Network Analysis
Motuzenko, K., & Makarov, I. (2023) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami58000.2023.10044509 · Cited by 2
Xiong, Y., Qiao, Y., Dong, S., Zhang, X., & Tan, H. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-9968-0_1 · Cited by 2
Extending scene-to-patch models: Multi-resolution multiple instance learning for Earth observation
Early, J., Chen, Y.-J., Evers, C., & Ramchurn, S. D. (2023) · Environmental Data Science · doi:10.1017/eds.2023.30 · Cited by 2
“However, in MIL, data are grouped into bags of instances, and only the bags are labeled, not the instances (Carbonneau et al., 2018).”
MethodBayesian multiple instance classification based on hierarchical probit regression
Xiong, D., Park, S., Lim, J., Wang, T., & Wang, X. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1780 · Cited by 2
MIVC: Multiple Instance Visual Component for Visual-Language Models
Wu, W., Li, Q., Zhong, W., & Huang, J. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00793 · Cited by 2
“Traditionally, Multiple Instance Learning (MIL) [5, 24] can be broadly categorized into two main types: (1) Bag-Level Prediction [4, 10, 13, 15]: In this approach, bag-level predictions are directly derived from instance-level predictions.”
BackgroundMethodVideo anomaly detection with long-and-short-term time series correlations
Zhu, X., Qian, X., Shi, Y., Tao, X., & Li, Z. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230406 · Cited by 2
Graph Neural Network Based Multi-Instance Learning with Graph Structure Learning
Liu, F., & Liu, W. (2024) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd62003.2024.10604509 · Cited by 2
“…no concept labels, while compared with unsupervised learning, the training bags in MIL have concept labels, so it is classified as a weakly supervised learning method [2], which can use the existing weak labeled data, avoiding a large number of manual labeling work costs in supervised learning.”
BackgroundAhmad, T. M., Guida, A., Stewart, S. A., Barrett, N., Vincer, M. J., & Afifi, J. K. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.09.003 · Cited by 2
“We implemented a simple network that combines 12 This framework is often used for weakly supervised learning tasks, such as classifying histopathologic slices.”
Renal tumor classification and detection based on artificial intelligence
Al-taie, R. H., & Hussein, N. J. (2025) · doi:10.1117/12.3058690 · Cited by 4
“Before using other image analysis techniques, it can be employed as a pre-processing step, however precaution should be used to avoid over-blurring and the loss of crucial information [14-15].”
Spatial Annotation-free Training for Sound Event Localization and Detection
Yasuda, M., Saito, S., Sato, N., & Harada, N. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889696 · Cited by 2
Hernandez, N. G., Carrillo‐Pérez, F., Ortuño, F. M., Rojas, I., & Valenzuela, O. (2025) · Cancers · doi:10.3390/cancers17091425 · Cited by 2
Ting, J. Y. C., & Barnard, A. S. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500033 · Cited by 4
United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
He, M., Duan, C., Xiao, P., Jia, T., Yu, S., Zhang, L., Hong, W., Han, J., Wu, Y., Li, Y., et al. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00061 · Cited by 6
Multiple Instance Learning through Explanation by Using a Histopathology Example
Konstantinov, A. V., & Utkin, L. V. (2022) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct54823.2022.9770901 · Cited by 2
Clinical profile prediction by multiple instance learning from multi-sensorial data
Tsirtsi, A., Zacharaki, E. I., Kalogiannis, S., & Megalooikonomou, V. (2019) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2019.8900761 · Cited by 2
“This formulation of weak supervision has gained ground, especially during the last years, where the volume of data has been increased radically [11].”
BackgroundTheory and Algorithms for Shapelet-Based Multiple-Instance Learning
Suehiro, D., Hatano, K., Takimoto, E., Yamamoto, S., Bannai, K., & Takeda, A. (2020) · Neural Computation · doi:10.1162/neco_a_01297 · Cited by 2
“MIL has been extensively studied both in theoretical and practical aspects [14, 2, 26, 39, 13, 6], since the notion of MIL was first proposed by Dietterich et al. [11].”
A Preliminary Study on Evolutionary Clustering for Multiple Instance Learning
Esteban, A., Zafra, A., & Ventura, S. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185588 · Cited by 2
Homicidal Event Forecasting and Interpretable Analysis Using Hierarchical Attention Model
Acharya, A., Krishnan, J., Arias, E. D., & Rangwala, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61255-9_14 · Cited by 2
Cai, X., Yu, D., Liu, D.-X., & Wu, M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2010/1/012108 · Cited by 2
“One of the common approaches for weakly-supervised SED is multiple instance learning (MIL)[9].”
BackgroundMachine Learning in Image Analysis and Pattern Recognition
Kumar, M., Sharma, R., & Sethi, I. K. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-1713-1 · Cited by 2
Multiple instance classification via quadratic programming
Küçükaşcı, E. Ş., Baydoğan, M. G., & Taşkın, Z. C. (2022) · Journal of Global Optimization · doi:10.1007/s10898-021-01120-0 · Cited by 2
“Current research areas of MIL include image classification, drug activity prediction, text mining and many others [5].”
BackgroundMethodAdewole, S., Fernandes, P., Jablonski, J. A., Copland, A. P., Porter, M. D., Syed, S., & Brown, D. E. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671281 · Cited by 2
Metadata
- Authors
- Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G.
- Affiliations
- École de Technologie Supérieure (CA)
- Erasmus MC (NL)
- Corresponding author
- Carbonneau, Marc-André
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 77
- Pages
- 329-353
- ISSN
- 0031-3203 (ISSN-L)
- 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
- FWCI
- 22.10
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- 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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