Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Open accessAbstract
Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.
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The paper's method achieves 86% sensitivity at 3 false positives per patient on mediastinal LN detection, surpassing prior state-of-the-art sensitivities of 78% and 70%.
“We obtain 86% sensitivity on 3 false positives (FP) per patient, versus the prior state-of-art sensitivities of 78% [36] (stacked shallow learning) and 70% [22] (CNN), as prior state-of-the-art.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
Many of our CNN models achieve notably better FROC-AUC and TPR/3FP results than previous state-of-the-art models for mediastinal LN detection; GoogLeNet-RI-L obtains AUC=0.95 and 0.85 TPR/3FP.
“GoogLeNet-RI-L | 0.95 | 0.85 | 0.91 | 0.69”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
The difference in performance lies in the fact that annotated LN segmentation masks are required to learn a mid-level semantic boundary detector, whereas CNN approaches only need LN locations for training.
“This difference lies in the fact that annotated lymph node segmentation masks are required to learn a mid-level semantic boundary detector [36], whereas CNN approaches only need LN locations for training [22].”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
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Cites
ImageNet classification with deep convolutional neural networks
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MethodGoing deeper with convolutions
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ResultRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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MethodLarge-Scale Video Classification with Convolutional Neural Networks
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · cited 1× · see citing works
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BackgroundMitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · cited 3× · see citing works
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MethodAccuracy of CT Colonography for Detection of Large Adenomas and Cancers
Johnson, C. D., Chen, M.-H., Toledano, A., Heiken, J., Dachman, A., Kuo, M. D., Menias, C., Siewert, B., Cheema, J. I. S., Obregon, R. G., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0800996 · Cited by 1,180 · cited 1× · see citing works
“In CT colonography (CTC), patients are typically scanned in the prone and supine positions [56], so we obtain two CT volumes per patient study.”
MethodMedical image classification with convolutional neural network
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., & Chen, M. (2014) · 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) · doi:10.1109/icarcv.2014.7064414 · Cited by 880 · cited 1× · see citing works
“Additionally, recent work has shown how the implementation of ConvNets can substantially improve the performance of state-of-the-art CADe systems [19]–[22].”
BackgroundMulti-scale Convolutional Neural Networks for Lung Nodule Classification
Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_46 · Cited by 635 · cited 1× · see citing works
“This strategy has been adapted to computer-aided pulmonary embolism detection ([52]), lung nodule classification ([11], [53]) in CT images and polyp detection in colonoscopy videos ([54], [55]).”
MethodComputer-aided detection and classification of microcalcifications in mammograms: a survey
Cheng, H.-D., Cai, X., Chen, X.-W., Hu, L.-M., & Lou, X. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00192-4 · Cited by 601 · cited 1× · see citing works
“At present, examples of CADe used in clinical practice include polyp detection for colon cancer screening [9], [10], lung nodule detection for lung cancer screening [11], [12] or breast cancer screening with mammography [13].”
MethodRoth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S.-J., Liu, J., Turkbey, E., & Summers, R. (2014) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-319-10404-1_65 · Cited by 593 · cited 6× · see citing works
“([20]), effectively circumvents the “curse-of-dimensionality” or “over-fitting” issue in the data-demanding ConvNet training procedures.”
BackgroundMethodInfluentialSelf-citationDeep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_31 · Cited by 582 · cited 3× · see citing works
“For instance, [19] proposes an MRI-based knee cartilage segmentation using a triplanar ConvNet.”
BackgroundMethodConvolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010) · Neural Computation · doi:10.1162/neco.2009.10-08-881 · Cited by 376 · cited 4× · see citing works
“Extensions of ConvNets to 3D have been proposed, but computational cost and memory consumption can be still too high to efficiently implement them on current computer graphics hardware units [23].”
BackgroundInfluentialComputed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.
Summers, R., Yao, J.-H., Pickhardt, P., Franaszek, M., Bitter, I., Brickman, D., Krishna, V., & Choi, J. R. (2005) · Gastroenterology · doi:10.1053/j.gastro.2005.08.054 · Cited by 316 · cited 10× · see citing works
“There were 79 training and 173 testing polyps ; and 22 training and 37 testing polyps ( , considered as large polyps) in our CTC dataset [27].”
BackgroundMethodInfluentialSelf-citationvan Ginneken, B., Setio, A. A. A., Jacobs, C., & Ciompi, F. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163869 · Cited by 278 · cited 2× · see citing works
“This strategy has been adapted to computer-aided pulmonary embolism detection ([52]), lung nodule classification ([11], [53]) in CT images and polyp detection in colonoscopy videos ([54], [55]).”
MethodGokturk, S., Tomasi, C., Acar, B., Beaulieu, C., Paik, D., Jeffrey, R. B. J., Yee, J., & Napel, S. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.974920 · Cited by 184 · cited 1× · see citing works
“tions of each ROI—an approach similar to [32].”
MethodColonic polyps: complementary role of computer-aided detection in CT colonography.
Summers, R., Jerebko, A., Franaszek, M., Malley, J., & Johnson, C. D. (2002) · Radiology · doi:10.1148/radiol.2252011619 · Cited by 159 · cited 1× · see citing works
“At present, examples of CADe used in clinical practice include polyp detection for colon cancer screening [9], [10], lung nodule detection for lung cancer screening [11], [12] or breast cancer screening with mammography [13].”
MethodSelf-citationComputer science: The learning machines
Jones, N. (2014) · Nature · doi:10.1038/505146a · Cited by 155 · cited 3× · see citing works
“These automatically learned filters need no tuning by hand, a major advantage over more traditional CADe approaches [19].”
BackgroundInfluentialAutomated spinal column extraction and partitioning
Yao, J.-H., O'Connor, S., & Summers, R. (2006) · IEEE International Symposium on Biomedical Imaging · doi:10.1109/isbi.2006.1624935 · Cited by 148
Self-citationFirmino, M., Morais, A. H. F., Mendoça, R. M., Dantas, M. R., Hekis, H. R., & Valentim, R. A. M. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-41 · Cited by 146 · cited 1× · see citing works
“At present, examples of CADe used in clinical practice include polyp detection for colon cancer screening [9], [10], lung nodule detection for lung cancer screening [11], [12] or breast cancer screening with mammography [13].”
MethodBayesian 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 · cited 2× · see citing works
“This type of cascade classification is similar in spirit to our presented second tier deep classifier (ConvNet), but uses state-of-the-art shallow classifiers (libSVM [47] and sparse linear fusion via the Relevance Vector Machine [48]).”
ResultTajbakhsh, N., Gotway, M., & Liang, J. (2015) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-319-24571-3_8 · Cited by 129 · cited 1× · see citing works
“This strategy has been adapted to computer-aided pulmonary embolism detection ([52]), lung nodule classification ([11], [53]) in CT images and polyp detection in colonoscopy videos ([54], [55]).”
MethodMechanisms of bone metastasis in prostate cancer: clinical implications
Msaouel, P., Pissimissis, N., Halapas, A., & Koutsilieris, M. (2008) · Best Practice & Research Clinical Endocrinology & Metabolism · doi:10.1016/j.beem.2008.01.011 · Cited by 116 · cited 1× · see citing works
“Furthermore, detection can help to assess the staging of a patient's disease, and thus has the potential to alter a patient's required treatment regimen [2].”
BackgroundAutomatic Detection and Segmentation of Lymph Nodes From CT Data
Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S. K., & Comaniciu, D. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2168234 · Cited by 94 · cited 1× · see citing works
“We use a soft-tissue window level of [-100, 200 HU] as in [44].”
MethodRoth, H. R., Yao, J.-H., Lu, L., Stieger, J., Burns, J. E., & Summers, R. (2014) · arXiv.org · doi:10.1007/978-3-319-14148-0_1 · Cited by 89 · see citing works
Self-citationComputer-Aided Detection of Polyps in CT Colonography Using Logistic Regression
van Ravesteijn, V. F., van Wijk, C., Vos, F., Truyen, R., Peters, J., Stoker, J., & van Vliet, L. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2028576 · Cited by 76 · cited 2× · see citing works
“At present, examples of CADe used in clinical practice include polyp detection for colon cancer screening [9], [10], lung nodule detection for lung cancer screening [11], [12] or breast cancer screening with mammography [13].”
BackgroundMethodFeulner, J., Kevin Zhou, S., Hammon, M., Hornegger, J., & Comaniciu, D. (2013) · Medical Image Analysis · doi:10.1016/j.media.2012.11.001 · Cited by 72 · cited 1× · see citing works
“Note that this shallow linear cascade approach via new image features, such as Histogram of Oriented Gradients, already significantly surpasses previous state-of-the-art methods [45], [46], [49].”
BackgroundStratified learning of local anatomical context for lung nodules in CT images
Wu, D., Lü, L., Bi, J., Shinagawa, Y., Boyer, K. L., Krishnan, A. V., & Salganicoff, 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.2010.5540008 · Cited by 69 · cited 2× · see citing works
“, intensity statistics, histogram of oriented gradients (HoG) [6], scale-invariant feature transform (SIFT) [7], Hessian based shape descriptors (such as blobness) [8], etc.”
BackgroundSelf-citationA Comprehensive Computer-Aided Polyp Detection System for Colonoscopy Videos
Tajbakhsh, N., Gurudu, S., & Liang, J. (2015) · Information Processing in Medical Imaging · doi:10.1007/978-3-319-19992-4_25 · Cited by 68 · cited 1× · see citing works
“This strategy has been adapted to computer-aided pulmonary embolism detection ([52]), lung nodule classification ([11], [53]) in CT images and polyp detection in colonoscopy videos ([54], [55]).”
MethodAutomated Detection of Sclerotic Metastases in the Thoracolumbar Spine at CT
Burns, J. E., Yao, J., Wiese, T., Munoz, H. E., Jones, E. C., & Summers, R. M. (2013) · Radiology · doi:10.1148/radiol.13121351 · Cited by 60 · cited 6× · see citing works
“In this study, we use three existing CADe systems that have previously been described in the literature: Detection of Sclerotic Spine Metastases: We use a recent CADe method for detecting sclerotic metastases candidates from CT volumes [4], [33] (see Section III-D).”
BackgroundMethodInfluentialSelf-citationAutomatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography
Hammon, M., Dankerl, P., Tsymbal, A., Wels, M., Kelm, M., May, M., Suehling, M., Uder, M., & Cavallaro, A. (2013) · European Radiology · doi:10.1007/s00330-013-2774-5 · Cited by 54 · cited 1×
“Therefore CADe has the potential to greatly reduce the radiologists' clinical workload and to serve as a first or second reader for improved assessment of the disease [3]–[5].”
Background2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
Seff, A., Lü, L., Cherry, K. M., Roth, H. R., Liu, J., Wang, S., Hoffman, J., Turkbey, E. B., & Summers, R. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_68 · Cited by 53 · cited 6× · see citing works
“ConvNets have a better chance of capturing the “essence” of the imaging data set used for training than do hand-crafted features [16], [6]–[8].”
BackgroundMethodInfluentialSelf-citationA Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions
Slabaugh, G., Yang, X.-Y., Ye, X.-J., Boyes, R., & Beddoe, G. (2010) · Algorithms · doi:10.3390/a3010021 · Cited by 45 · cited 7×
“The colonic polyp CADe dataset scales are similar: 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation) in [37], [40]; 180 patients (360 CTC volumes) for training and 202 patients (404 volumes) for testing [38].”
BackgroundMethodResultInfluentialA Statistical Parts-Based Model of Anatomical Variability
Toews, M., & Arbel, T. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892510 · Cited by 41 · see citing works
Automatic mediastinal lymph node detection in chest CT
Feuerstein, M., Deguchi, D., Kitasaka, T., Iwano, S., Imaizumi, K., Hasegawa, Y., Suenaga, Y., & Mori, K. (2009) · Medical Imaging · doi:10.1117/12.811101 · Cited by 37 · cited 1× · see citing works
“Note that this shallow linear cascade approach via new image features, such as Histogram of Oriented Gradients, already significantly surpasses previous state-of-the-art methods [45], [46], [49].”
BackgroundEmploying topographical height map in colonic polyp measurement and false positive reduction
Yao, J., Li, J., & Summers, R. M. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.09.034 · Cited by 36 · cited 2× · see citing works
“Potentially, the performance could be improved by adopting a local colonic surface alignment, such as [39], to further guide and constrain our random view sampling procedure.”
BackgroundMethodSelf-citationOptimizing the support vector machines (SVM) committee configuration in a colonic polyp CAD system
Yao, J.-H., Summers, R., & Hara, A. (2005) · SPIE Medical Imaging · doi:10.1117/12.594547 · Cited by 36 · cited 2×
“Last, a committee of SVMs [34] is trained on these features.”
BackgroundSelf-citationLiu, J., Zhao, J., Hoffman, J., Yao, J.-H., Zhang, W., Turkbey, E., Wang, S.-J., Kim, C. F., & Summers, R. (2014) · Medical Imaging · doi:10.1117/12.2043737 · Cited by 33 · cited 6× · see citing works
“There are a total of 1956 true-positive detections from [26], [25].”
BackgroundMethodInfluentialSelf-citationEffective 3D object detection and regression using probabilistic segmentation features in CT images
Lü, L., Bi, J., Wolf, M., & Salganicoff, M. (2011) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2011.5995359 · Cited by 31 · cited 6× · see citing works
“The colonic polyp CADe dataset scales are similar: 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation) in [37], [40]; 180 patients (360 CTC volumes) for training and 202 patients (404 volumes) for testing [38].”
BackgroundMethodResultInfluentialSelf-citationAbdominal lymphadenopathy detection using random forest
Cherry, K. M., Wang, S.-J., Turkbey, E., & Summers, R. (2014) · Medical Imaging · doi:10.1117/12.2043837 · Cited by 25 · cited 9× · see citing works
“There are a total of 1956 true-positive detections from [26], [25].”
BackgroundMethodInfluentialSelf-citationComputer aided lytic bone metastasis detection using regular CT images
Yao, J.-H., O'Connor, S., & Summers, R. (2006) · SPIE Medical Imaging · doi:10.1117/12.652288 · Cited by 25 · cited 1×
“Axial 2D cross sections of the vertebrae are then divided into sub-segments using a watershed algorithm based on local density differences [33].”
MethodSelf-citationComputer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation
Lü, L., Devarakota, P., Vikal, S., Wu, D., Zheng, Y., & Wolf, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05530-5_16 · Cited by 24 · cited 6× · see citing works
“The colonic polyp CADe dataset scales are similar: 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation) in [37], [40]; 180 patients (360 CTC volumes) for training and 202 patients (404 volumes) for testing [38].”
BackgroundMethodResultInfluentialSelf-citationDetection of sclerotic bone metastases in the spine using watershed algorithm and graph cut
Wiese, T., Yao, J.-H., Burns, J. E., & Summers, R. (2012) · Medical Imaging · doi:10.1117/12.911700 · Cited by 23 · cited 1×
“Therefore CADe has the potential to greatly reduce the radiologists' clinical workload and to serve as a first or second reader for improved assessment of the disease [3]–[5].”
BackgroundSelf-citationA Two-Level Approach Towards Semantic Colon Segmentation: Removing Extra-Colonic Findings
Lu, L., Wolf, M., Liang, J., Dundar, M., Bi, J., & Salganicoff, M. (2009) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-642-04271-3_122 · Cited by 21 · cited 1×
“There exist two types of cascaded CADe classification architectures for false positive reduction are two types: 1) extraction of new image features followed by retraining of a classifier on all candidates [39], [38], [6], [20], [40] (from Section II-D) or 2) design of application dependent post-filtering components [4…”
MethodSelf-citationCoarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
Lu, L., Liu, M., Ye, X.-J., Yu, S.-P., & Huang, H. (2011) · International Conference on Information and Knowledge Management · doi:10.1145/2063576.2064004 · Cited by 18 · cited 1× · see citing works
“This is a coarse-to-fine classification approach slightly inspired by other CADe schemes such as presented in [24] although our methods are significantly different.”
MethodSelf-citationSimultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography
Lü, L., Barbu, A., Wolf, M., Liang, J., Bogoni, L., Salganicoff, M., & Comaniciu, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_34 · Cited by 17 · cited 1× · see citing works
“3D flexible tubes [41], ileo-cecal valve [42] and extra-colonic”
Self-citationWiese, T., Burns, J. E., Yao, J.-H., & Summers, R. (2011) · IEEE International Symposium on Biomedical Imaging · doi:10.1109/isbi.2011.5872376 · Cited by 17 · cited 1×
“In this study, we use three existing CADe systems that have previously been described in the literature: Detection of Sclerotic Spine Metastases: We use a recent CADe method for detecting sclerotic metastases candidates from CT volumes [4], [33] (see Section III-D).”
MethodSelf-citationNakamura, Y., Nimura, Y., Kitasaka, T., Mizuno, S., Furukawa, K., Goto, H., Fujiwara, M., Misawa, K., Ito, M., Nawano, S., et al. (2013) · Medical Imaging · doi:10.1117/12.2008282 · Cited by 12 · cited 2×
“In the abdomen, the most recent work ([46]) shows a 70.”
BackgroundHierarchical Part-Based Detection of 3D Flexible Tubes: Application to CT Colonoscopy
Barbu, A., Bogoni, L., & Comaniciu, D. (2006) · Lecture notes in computer science · doi:10.1007/11866763_57 · Cited by 9 · cited 2× · see citing works
“3D flexible tubes [41], ileo-cecal valve [42] and extra-colonic”
MethodSrivastava (2014) · doi:10.5555/2627435.2670313 · cited 1×
“It is a variation of the previously suggested “DropOut” method [29], [30].”
Method
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
“The incorpora- ion of contextual or 3D information is also handled using multi- tream CNNs ( Section 2.4.2 , for example by Barbu et al., 2016 and oth et al., 2016b ).”
MethodInfluentialShin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
Self-citationDeep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
“…generating samples via affine transformation (i.e., data augmentation) and then train their network from scratch with the augmented dataset (35, 36, 37, 38); (iii) to use deep models trained over a huge number of natural images in computer vision as ‘off-the-shelf’ feature extractor and then…”
MethodOpportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000 · see citing works
“Implicit features could determine fibroids and polyps [1], and characterize irregularities in tissue morphology such as tumors [2].”
BackgroundFrid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 1,952 · see citing works
“T HE greatest challenge in the medical imaging domain is how to cope with the small datasets and limited amount of annotated samples [1]–[5], especially when employing supervised machine learning algorithms that require labeled data and larger training examples.”
BackgroundMethodDeep learning in bioinformatics
Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,514 · see citing works
“[125] applied CNNs to three different CT image datasets to classify sclerotic metastases, lymph nodes and colonic polyps.”
MethodDeep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works
Medical image analysis using convolutional neural networks: a review
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1088-1 · Cited by 1,431 · see citing works
“The study presented in [98] aims at improving computer aided detection using a CNN and random view generation.”
MethodZhou, S. K., Greenspan, H. K., Davatzikos, C. A., Duncan, J. S., van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,294 · see citing works
“Studies have assessed abdominal lymph nodes on CT in general and for specific diseases, such as prostate cancer [35], [199], [200].”
BackgroundSelf-citationDeep Learning: A Primer for Radiologists
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017) · RadioGraphics · doi:10.1148/rg.2017170077 · Cited by 1,115 · see citing works
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M. A., Thai, T., Moore, K. N., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,129 · see citing works
Road crack detection using deep convolutional neural network
Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533052 · Cited by 1,602 · see citing works
“Inspired by the method proposed in [11], the probability of a point can be calculated by averaging probability {P1, .”
MethodGreenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
“, [13] axial, coronal and sagittal images were taken centered on a voxel in a colonic polyp or lymph node candidate and fed into the cudaconvnet CNN, which incorporates three channels normally used to represent the red, green and blue color channels of a natural light image.”
MethodSelf-citationMR-based synthetic CT generation using a deep convolutional neural network method
Han, X. (2017) · Medical Physics · doi:10.1002/mp.12155 · Cited by 872 · see citing works
Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · see citing works
“In the context of CAD, most works focused on the problem of abnormality detection (CADe) 36–38 .”
BackgroundApplications of Deep Learning and Reinforcement Learning to Biological Data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 852 · see citing works
“76]) in detecting colon polyp from CT colonographs [137].”
BackgroundMethodDeep learning in medical imaging and radiation therapy
Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 739 · see citing works
“166,168 More recently, 3D CNNs that use 3D convolution kernels are successfully replacing 2D CNNs for volumetric data.”
BackgroundSelf-citationAutomatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works
“Most previous approaches adapted 2D CNNs for processing 3D volumetric data [18], [45], [46], with difficulties being reported when attempting to employ 3D CNNs.”
MethodCovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-turjman, F., & Pinheiro, P. (2020) · IEEE Access · doi:10.1109/access.2020.2994762 · Cited by 659 · see citing works
“With limited quantity and variety of samples, the biggest challenge in the medical imaging domain is small datasets [5]–[7].”
BackgroundGiger (2018) · doi:10.1016/j.jacr.2017.12.028 · Cited by 634 · see citing works
Yan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.036501 · Cited by 573 · see citing works
“7 – 9 Instead, they reflect the relative performance of different types and sizes.”
BackgroundResultSelf-citationMazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works
“thors performed detection of 3D anatomy in chest CT images by processing data slice by slice in one direction. Combining output from dierent planes was performed in several studies. Most of the them [102, 103, 104] used orthogonal planes of MRI and CT images performing detection in each direction separately. The resul…”
MethodRoad Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Fan, R., Bocus, M. J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., & Liu, M. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814000 · Cited by 240 · see citing works
“Furthermore, some machine learning-based crack detection approaches [30]–[35] have been proposed in recent years, and the features produced by neural network are very likely to replace the local features utilized in traditional”
BackgroundFang, L., Cunefare, D., Wang, C., Guymer, R. H., Shutao Li, A., & Farsiu, S. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.002732 · Cited by 481 · see citing works
“In this paper, we use a modified Cifar-CNN [38,57] architecture.”
MethodDeepPap: Deep Convolutional Networks for Cervical Cell Classification
Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., & Yao, J. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705583 · Cited by 495 · see citing works
“They have also significantly improved performance in a variety of medical imaging applications [33], [34], such as classification of lung diseases and lymph nodes in CT images [35], [36], segmentation (pixel classification) of brain tissues [37] in MRI, vessel segmentation [38] in fundus images, and detecting cervical…”
BackgroundMethodSelf-citationBreast cancer detection using deep convolutional neural networks and support vector machines
Ragab, D., Sharkas, M., Marshall, S., & Ren, J.-C. (2019) · PeerJ · doi:10.7717/peerj.6201 · Cited by 438 · see citing works
“There are many CNN architectures such as CiFarNet ( Krizhevsky, 2009 ; Roth et al., 2016 ), AlexNet ( Krizhevsky, Sutskever & Hinton, 2012 ), GoogLeNet ( Szegedy et al., 2015 ), The AlexNet architecture achieved significantly better performance over the other deep learning methods for ImageNet…”
MethodArtificial intelligence in oncology
Shimizu, H., & Nakayama, K. I. (2020) · Cancer Science · doi:10.1111/cas.14377 · Cited by 388 · see citing works
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
Chang, P. D., Grinband, J., Weinberg, B. D., Bardis, M. D., Khy, M., Cadena, G., Su, M., Cha, S., Filippi, C. G., Bota, D. A., et al. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5667 · Cited by 466 · see citing works
“24-26 Recent use of CNNs has started to yield promising results in multiple medical imaging disciplines, including the detection of pulmonary nodules, 27 colon cancer, 28 and cerebral microbleeds.”
BackgroundLymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning
Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., et al. (2020) · Radiology · doi:10.1148/radiol.2019190372 · Cited by 384 · see citing works
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358 · see citing works
“problem of abnormality detection (CADe)[56, 57, 58].”
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works
Focus: Querying Large Video Datasets with Low Latency and Low CostPreprint
Hsieh, K., Ananthanarayanan, G., Bodík, P., Venkataraman, S., Bahl, P., Philipose, M., Gibbons, P. B., & Mutlu, O. (2018) · USENIX Symposium on Operating Systems Design and Implementation · doi:10.48550/arxiv.1801.03493 · Cited by 313 · see citing works
“, [57, 72]), information decoding in biomedical signal recordings (e.”
BackgroundShafique, S., & Tehsin, S. (2018) · Technology in Cancer Research and Treatment · doi:10.1177/1533033818802789 · Cited by 311 · see citing works
“So lot of groups in medical imaging deployed DCNNs and they were able to achieve good accuracy.(10-15) As per our knowledge for leukemia detection only, Vogado et al(16) investigated a pretrained CNN for the features extraction and used ensemble classifiers for classification of normal and blast cells.”
MethodSurvey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
Moon, W. K., Lee, Y., Ke, H.-H., Lee, S. H., Huang, C., & Chang, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105361 · Cited by 322 · see citing works
Designing deep learning studies in cancer diagnostics
Kleppe, A., Skrede, O.-J., Raedt, S. D., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-020-00327-9 · Cited by 318 · see citing works
Classification of teeth in cone-beam CT using deep convolutional neural network
Miki, Y., Muramatsu, C., Hayashi, T., Zhou, X.-R., Hara, T., Katsumata, A., & Fujita, H. (2017) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2016.11.003 · Cited by 301 · see citing works
A deep feature based framework for breast masses classification
Jiao, Z., Gao, X., Wang, Y., & Li, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.060 · Cited by 304 · see citing works
“According to the successful application of [60] on medical image and followed the diagnosis habits of the radiologist who we consulted from, instances we applied had been rotated angles of 90 ° , 180 ° , 270 ° to enlarge the dataset 3 times.”
MethodLiterature review: efficient deep neural networks techniques for medical image analysis
Abdou, M. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06960-9 · Cited by 315 · see citing works
“New features can lead to missing information within the first stage and thus could lead to a better classification; FPs could be then reduced to the minimum extent [30].”
BackgroundRoth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.006 · Cited by 325 · see citing works
Self-citationAubreville, M., Knipfer, C., Oetter, N., Jaremenko, C., Rodner, E., Denzler, J., Bohr, C., Neumann, H., Stelzle, F., & Maier, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-12320-8 · Cited by 328 · see citing works
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242 · see citing works
Saba, T. (2020) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2020.06.033 · Cited by 298 · see citing works
Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography
González, G., Ash, S. Y., Vegas‐Sánchez‐Ferrero, G., Onieva, J. O., Rahaghi, F. N., Ross, J. C., Díaz, A. A., Estépar, R. S. J., & Washko, G. R. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201705-0860oc · Cited by 280 · see citing works
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
Tucker, A., Wang, Z., Rotalinti, Y., & Myles, P. R. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00353-9 · Cited by 230 · see citing works
Liang, G., Hong, H., Xie, W., & Zheng, L. (2018) · IEEE Access · doi:10.1109/access.2018.2846685 · Cited by 287 · see citing works
“They also significantly improve the performance of various medical imaging applications [30], [31], such as classification of lung diseases and lymph nodes in CT images [32], [33], segmentation (pixel classification) of brain tissues in MRI [34], vessel segmentation based on fundus images [37], and detecting cervical…”
BackgroundDeep learning based classification of breast tumors with shear-wave elastography
Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J., & Zheng, H. (2016) · Ultrasonics · doi:10.1016/j.ultras.2016.08.004 · Cited by 240 · see citing works
Fully Convolutional Network for Liver Segmentation and Lesions Detection
Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., & Greenspan, H. (2016) · LABELS/DLMIA@MICCAI · doi:10.1007/978-3-319-46976-8_9 · Cited by 236 · see citing works
“CNN has been used for detection in several medical applications including pulmonary nodule [10], sclerotic metastases, lymph node and colonic polyp [8] and liver tumours [6].”
MethodGenerative Adversarial Network for Medical Images (MI-GAN)
Iqbal, T., & Ali, H. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1072-9 · Cited by 255 · see citing works
Asaoka, R., Murata, H., Hirasawa, K., Fujino, Y., Matsuura, M., Miki, A., Kanamoto, T., Ikeda, Y., Mori, K., Iwase, A., et al. (2018) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2018.10.007 · Cited by 255 · see citing works
Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Napel, S. A., Mu, W., Jardim‐Perassi, B. V., Aerts, H. J. W. L., & Gillies, R. J. (2018) · Cancer · doi:10.1002/cncr.31630 · Cited by 229 · see citing works
“…from the 3D medical images, information from 2D patches of the 3 orthogonal views (axial, sagittal, and coronal) can be used, which is a method referred to as 2.5D. 115 These 3 views can be inputted into existing CNNs trained on 3 channels (often the red-green-blue channels of natural images).”
MethodArtificial Intelligence and Acute Stroke Imaging
Soun, J. E., Chow, D. S., Nagamine, M., Takhtawala, R. S., Filippi, C. G., Yu, W., & Chang, P. D. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6883 · Cited by 302 · see citing works
“12 CNNs have already shown promise in the detection of pulmonary nodules, 13 colon cancer, 14 and cerebral microbleeds.”
BackgroundWang, H.-K., Zhou, Z., Li, Y.-C., Chen, Z.-H., Lu, P., Wang, W., Liu, W.-Y., & Yu, L.-J. (2017) · EJNMMI Research · doi:10.1186/s13550-017-0260-9 · Cited by 211 · see citing works
Sun, W., Zheng, B., & Qian, W. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.006 · Cited by 223 · see citing works
Deep Learning and Convolutional Neural Networks for Medical Image Computing
Lu, L., Zheng, Y., Carneiro, G., & Yang, L. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-42999-1 · Cited by 208 · see citing works
“There are also several extensions from the decompositional view representation [27, 28], such as using a novel vessel-alignedmulti-planar image representation for pulmonary embolismdetection [29], fusing unregisteredmultiview for mammogram analysis [16], and classifying pulmonary perifissural nodules via an ensemble o…”
BackgroundMethodInfluentialSelf-citationLeger, S., Zwanenburg, A., Pilz, K., Lohaus, F., Linge, A., Zöphel, K., Kotzerke, J., Schreiber, A., Tinhofer, I., Budach, V., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13448-3 · Cited by 217 · see citing works
“The application of CNNs have already showed promising results in the medical imaging domain 37 such as image segmentation 38 or lesion detection 39 .”
MethodGuo, S., Zhang, B., Yang, T., Lyu, D., & Gao, W. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2942548 · Cited by 224 · see citing works
“With great successes of CNN in image recognition [9], object detection [10], action recognition [11], and disease diagnosis [12], CNN is also widely used in fault diagnosis.”
BackgroundSpatio-temporal deep learning method for ADHD fMRI classification
Mao, Z., Su, Y., Xu, G., Wang, X., Huang, Y., Yue, W., Sun, L., & Xiong, N. N. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.043 · Cited by 194 · see citing works
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., & Roth, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093608 · Cited by 170 · see citing works
“Moreover, [33, 41] illustrates the effectiveness of multiview training on 2D slices, even by simply averaging multiplanar results, indicating complementary latent information exists in the biases of 2D networks.”
BackgroundSelf-citationZhang, R., Zheng, Y., Poon, C. C. Y., Shen, D. G., & Lau, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.026 · Cited by 179 · see citing works
“The algorithm will be useful to the emerging therapeutic wireless endoscopic capsules [38], or to integrate with classifier that judges whether a polyp should be resected or not [15].”
BackgroundMethodInterpretation and visualization techniques for deep learning models in medical imaging
Huff, D. T., Weisman, A. J., & Jeraj, R. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/abcd17 · Cited by 172 · see citing works
Iakovidis, D. K., Georgakopoulos, S. V., Vasilakakis, M. D., Koulaouzidis, A., & Plagianakos, V. P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837002 · Cited by 194 · see citing works
“classifiers, trained using image patches [8], [11]–[13], [27], [28], [31]–[34] and as weakly-supervised classifiers trained”
MethodTajbakhsh, N., & Suzuki, K. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.029 · Cited by 170 · see citing works
Deep learning workflow in radiology: a primer
Montagnon, E., Cerny, M., Cadrin-Chênevert, A., Hamilton, V., Derennes, T., Ilinca, A. P., Vandenbroucke‐Menu, F., Turcotte, S., Kadoury, S., & Tang, A. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0832-5 · Cited by 187 · see citing works
“In abdominal radiology, deep learning has been applied to diverse tasks [3], organs [5, 6], and pathologies [7 – 9].”
MethodArtificial intelligence in oral and maxillofacial radiology: what is currently possible?
Heo, M.-S., Kim, J., Hwang, J. J., Han, S., Kim, J.-S., Yi, W.-J., & Park, I.-W. (2020) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20200375 · Cited by 190 · see citing works
Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification
Ribeiro, E. F., Uhl, A., Wimmer, G., & Häfner, M. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/6584725 · Cited by 167 · see citing works
“Additionally, recent studies show the potential of the knowledge transfer between natural and medical images using pretrained (off-the-shelf) CNNs [34, 46]. important object of research in clinical decision support system area.”
BackgroundNishio, M., Sugiyama, O., Yakami, M., Ueno, S., Kubo, T., Kuroda, T., & Togashi, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200721 · Cited by 170 · see citing works
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Domingues, I., Pereira, G. C., Martins, P., Duarte, H., Santos, J. A. M., & Abreu, P. H. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09788-3 · Cited by 173 · see citing works
Machine learning and deep learning methods that use omics data for metastasis prediction
Albaradei, S., Thafar, M. A., Alsaedi, A., Neste, C. V., Gojobori, T., Essack, M., & Gao, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.001 · Cited by 165 · see citing works
“Some methods incorporated clinicopathological-based features [20–22], other methods incorporated image-based features [23– 25] or text-based features [26], while the more recent methods incorporate omics-based data as features.”
MethodDas, N., Hussain, E., & Mahanta, L. B. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.05.003 · Cited by 168 · see citing works
Bhati, D., Neha, F., & Amiruzzaman, M. (2024) · Journal of Imaging · doi:10.3390/jimaging10100239 · Cited by 166 · see citing works
“In medical imaging, filter visualization compares filters in Computer-Aided Detection (CAD) for 3D Computed Tomography (CT) images [9].”
MethodDeep learning and its application to medical image segmentation
Roth, H. R., Shen, C., Oda, H., Oda, M., Hayashi, Y., Misawa, K., & Mori, K. (2018) · arXiv.org · doi:10.11409/mit.36.63 · Cited by 136
“A few examples in radiology are anatomy classification [10], [11], false-positives reduction for computer-aided detection of lymph nodes or colonic polyps [12], the detection of pulmonary nodules [13], and the detection of pulmonary embolisms [14].”
BackgroundSelf-citation3D Context Enhanced Region-Based Convolutional Neural Network for End-to-End Lesion Detection
Yan, K., Bagheri, M. H., & Summers, R. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_58 · Cited by 109 · see citing works
“Some researchers trained classifiers on the aggregation of multiple 2D slices (e.g. three orthogonal views (2.5D) or random views of the candidate lesion) for FPR [9].”
BackgroundMethodSelf-citationIslam, M. M. M., & Kim, J.-M. (2019) · Sensors · doi:10.3390/s19194251 · Cited by 137 · see citing works
“Recently, Zhang et al. [26] used a fully convolutional network with a residual network as a backbone for concrete surface crack detection.”
BackgroundSemantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., & Mougiakakou, S. G. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2818620 · Cited by 152 · see citing works
“5D, [46].”
Hamraz, H., Jacobs, N., Contreras, M. A., & Clark, C. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.10.011 · Cited by 115 · see citing works
Xu, L., Tetteh, G., Lipková, J., Zhao, Y. S., Li, H., Christ, P. F., Piraud, M., Buck, A. K., Shi, K., & Menze, B. H. (2018) · Contrast Media & Molecular Imaging · doi:10.1155/2018/2391925 · Cited by 126 · see citing works
“They have already demonstrated advantages in computerized diagnosis on medical images, such as abnormality detection [42, 43].”
BackgroundDeep Learning in Visual Computing and Signal Processing
Xie, D., Zhang, L., & Li, B. (2017) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2017/1320780 · Cited by 118 · see citing works
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- Authors
- Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M.
- Affiliations
- National Institutes of Health Clinical Center (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1170-1181
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC7340334
- OpenAlex
- W1871050032
- Cited by
- 608
- References
- 60
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 35 · 2023: 34 · 2022: 72 · 2021: 101 · 2020: 88 · 2019: 84 · 2018: 71 · 2017: 62 · 2016: 27 · 2015: 1
- FWCI
- 52.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (public-domain)
- Funders
- National Institutes of Health
- NIH Clinical Center (Z01CL040004, Z01CL040003)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- computer-aided detection, false positive reduction, convolutional neural networks, lymph node detection, colorectal polyp detection, scale transformation
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Child, Colonic Polyps, Female, Humans, Lymph Nodes, Male, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Spinal Neoplasms, Tomography, X-Ray Computed, Databases, Factual, Neural Networks, Computer, Young Adult
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