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Medical images differ from natural images in significantly higher resolutions and smaller regions of interest.
Deep neural networks improve radiologists’ performance in breast cancer screening
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Efficientnet: Rethinking model scaling for convolutional neural networks
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Deep angular embedding and feature correlation attention for breast mri cancer analysis
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Pseudoedgenet: Nuclei segmentation only with point annotations
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Improving localization-based approaches for breast cancer screening exam classification
Févry, T., Phang, J., Wu, N., Kim, S., Moy, L., Cho, K., Geras, K.J., 2019 · 1908
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Mammographic breast density and risk of breast cancer: masking bias or causality?
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Ensemble methods in machine learning, in: International Workshop on Multiple Classifier Systems, Springer. pp. 1–15
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False-positive reduction in cad mass detection using a competitive classification strategy
Li, L., Zheng, Y., Zhang, L., Clark, R.A., 2001 · 2001
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The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties: a collaborative evaluation
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Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality
Kopans, D.B., 2002 · 2002
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Breast imaging reporting and data system (bi-rads)
Liberman, L., Menell, J.H., 2002 · 2002
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Bilateral analysis based false positive reduction for computer-aided mass detection
Wu, Y.T., Wei, J., Hadjiiski, L.M., Sahiner, B., Zhou, C., Ge, J., Shi, J., Zhang, Y., Chan, H.P., 2007 · 2007
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Imagenet: A large-scale hierarchical image database, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Ieee. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
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Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features
Masotti, M., Lanconelli, N., Campanini, R., 2009 · 2009
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The spatial distribution of radiodense breast tissue: a longitudinal study
Pereira, S.M.P., McCormack, V.A., Moss, S.M., dos Santos Silva, I., 2009 · 2009
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A review of automatic mass detection and segmentation in mammographic images
Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E.R., Zwiggelaar, R., 2010 · 2010
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Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study
Wei, J., Chan, H.P., Wu, Y.T., Zhou, C., Helvie, M.A., Tsodikov, A., Hadjiiski, L.M., Sahiner, B., 2011 · 2011
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Random search for hyper-parameter optimization
Bergstra, J., Bengio, Y., 2012 · 2012
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ACR BI-RADS atlas: breast imaging reporting and data system
D’Orsi, C.J., 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., Bengio, Y., 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A., 2014 · 2014
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Weakly supervised histopathology cancer image segmentation and classification
Xu, Y., Zhu, J.Y., Eric, I., Chang, C., Lai, M., Tu, Z., 2014 · 2014
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Deep learning, sparse coding, and svm for melanoma recognition in dermoscopy images, in: International Workshop on Machine Learning in Medical Imaging, Springer. pp. 118–126
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., Smith, J.R., 2015 · 2015
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An open letter to panels that are deciding guidelines for breast cancer screening
Kopans, D.B., 2015 · 2015
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Deep learning
LeCun, Y., Bengio, Y., Hinton, G., 2015 · 2015
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Diagnostic accuracy of digital screening mammography with and without computer-aided detection
Lehman, C.D., Wellman, R.D., Buist, D.S., Kerlikowske, K., Tosteson, A.N., Miglioretti, D.L., 2015 · 2015
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Effective approaches to attention-based neural machine translation
Luong, M.T., Pham, H., Manning, C.D., 2015 · 2015
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Is object localization for free?-weakly-supervised learning with convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 685–694
Oquab, M., Bottou, L., Laptev, I., Sivic, J., 2015 · 2015
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From image-level to pixel-level labeling with convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1713–1721
Pinheiro, P.O., Collobert, R., 2015 · 2015
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Improving breast cancer detection using symmetry information with deep learning, in: Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer, pp. 90–97
Hagos, Y.B., Mérida, A.G., Teuwen, J., 2018 · 2018
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Deep learning in mammography and breast histology, an overview and future trends
Hamidinekoo, A., Denton, E., Rampun, A., Honnor, K., Zwiggelaar, R., 2018 · 2018
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Attention-based deep multiple instance learning
Ilse, M., Tomczak, J.M., Welling, M., 2018 · 2018
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Applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study
Kim, E.K., Kim, H.E., Han, K., Kang, B.J., Sohn, Y.M., Woo, O.H., Lee, C.W., 2018 · 2018
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Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image Computing and Computer-Assisted Intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Anatomy-specific classification of medical images using deep convolutional nets
Roth, H.R., Lee, C.T., Shin, H.C., Seff, A., Kim, L., Yao, J., Lu, L., Summers, R.M., 2015 · 2015
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Weakly supervised deep detection networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2846–2854
Bilen, H., Vedaldi, A., 2016 · 2016
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An analysis of deep neural network models for practical applications
Canziani, A., Paszke, A., Culurciello, E., 2016 · 2016
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L., 2016 · 2016
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National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium
Lehman, C.D., Arao, R.F., Sprague, B.L., Lee, J.M., Buist, D.S., Kerlikowske, K., Henderson, L.M., Onega, T., Tosteson, A.N., Rauscher, G.H., et al., 2016 · 2016
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Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016 · 2016
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Kyono, T., Gilbert, F.J., van der Schaar, M., 2018 · 2018
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Detecting and classifying lesions in mammograms with deep learning
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I., 2018 · 2018
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Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x-ray images, in: International Workshop on Machine Learning in Medical Imaging, Springer. pp. 267–275
Sedai, S., Mahapatra, D., Ge, Z., Chakravorty, R., Garnavi, R., 2018 · 2018
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Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7268–7277
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S., 2018 · 2018
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Breast density classification with deep convolutional neural networks, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE. pp. 6682–6686
Wu, N., Geras, K.J., Shen, Y., Su, J., Kim, S.G., Kim, E., Wolfson, S., Moy, L., Cho, K., 2018 · 2018
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Weakly supervised medical diagnosis and localization from multiple resolutions
Yao, L., Prosky, J., Poblenz, E., Covington, B., Lyman, K., 2018 · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Silva, V.W.K., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J., 2019 · 2019
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New frontiers: An update on computer-aided diagnosis for breast imaging in the age of artificial intelligence
Gao, Y., Geras, K.J., Lewin, A.A., Moy, L., 2019 · 2019
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Artificial intelligence for mammography and digital breast tomosynthesis: Current concepts and future perspectives
Geras, K.J., Mann, R.M., Moy, L., 2019 · 2019
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Deep local-global refinement network for stent analysis in ivoct images, in: International Conference on Medical image Computing and Computer-Assisted Intervention, Springer. pp. 539–546
Guo, Y., Bi, L., Kumar, A., Gao, Y., Zhang, R., Feng, D., Wang, Q., Kim, J., 2019 · 2019
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Scribble-based hierarchical weakly supervised learning for brain tumor segmentation, in: International Conference on Medical image Computing and Computer-Assisted Intervention, Springer. pp. 175–183
Ji, Z., Shen, Y., Ma, C., Gao, M., 2019 · 2019
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Constrained-cnn losses for weakly supervised segmentation
Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B., 2019 · 2019
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Weakly supervised mitosis detection in breast histopathology images using concentric loss
Li, C., Wang, X., Liu, W., Latecki, L.J., Wang, B., Huang, J., 2019 · 2019
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Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision, in: International Conference on Computer Vision, pp. 10632–10641
Liu, J., Zhao, G., Fei, Y., Zhang, M., Wang, Y., Yu, Y., 2019 · 2019
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Weakly supervised segmentation framework with uncertainty: A study on pneumothorax segmentation in chest x-ray, in: International Conference on Medical image Computing and Computer-Assisted Intervention, Springer. pp. 613–621
Ouyang, X., Xue, Z., Zhan, Y., Zhou, X.S., Wang, Q., Zhou, Y., Wang, Q., Cheng, J.Z., 2019 · 2019
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Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
Rampun, A., López-Linares, K., Morrow, P.J., Scotney, B.W., Wang, H., Ocaña, I.G., Maclair, G., Zwiggelaar, R., Ballester, M.A.G., Macía, I., 2019 · 2019
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Attention gated networks: Learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D., 2019 · 2019
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Globally-aware multiple instance classifier for breast cancer screening, in: Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with International Conference on Medical image Computing and Computer-Assisted Intervention 2019, Shenzhen, China, October 13, 2019, Proceedings, Springer. p. 18
Shen, Y., Wu, N., Phang, J., Park, J., Kim, G., Moy, L., Cho, K., Geras, K.J., 2019 · 2019
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Cancer statistics, 2019
Siegel, R.L., Miller, K.D., Jemal, A., 2019 · 2019
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Learning from suspected target: Bootstrapping performance for breast cancer detection in mammography, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 468–476
Xiao, L., Zhu, C., Liu, J., Luo, C., Liu, P., Zhao, Y., 2019 · 2019
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Joint learning of saliency detection and weakly supervised semantic segmentation, in: International Conference on Computer Vision, pp. 7223–7233
Zeng, Y., Zhuge, Y., Lu, H., Zhang, L., 2019 · 2019
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Learning instance activation maps for weakly supervised instance segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3116–3125
Zhu, Y., Zhou, Y., Xu, H., Ye, Q., Doermann, D., Jiao, J., 2019 · 2019
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International evaluation of an ai system for breast cancer screening
McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G.C., Darzi, A., et al., 2020 · 2020
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