Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
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Abstract
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.
Keywords
Deep learning Neural networks Breast cancer screening Weakly supervised localization High-resolution image classificationReferences
- 1.Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)MathSciNetzbMATHGoogle Scholar
- 2.Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)Google Scholar
- 3.Diba, A., Sharma, V., Pazandeh, A.M., Pirsiavash, H., Van Gool, L.: Weakly supervised cascaded convolutional networks. In: CVPR (2017)Google Scholar
- 4.Durand, T., Mordan, T., Thome, N., Cord, M.: Wildcat: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: CVPR (2017)Google Scholar
- 5.Gao, Y., Geras, K.J., Lewin, A.A., Moy, L.: New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence. Am. J. Roentgenol. 212(2), 300–307 (2019)CrossRefGoogle Scholar
- 6.Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv:1802.04712 (2018)
- 7.Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
- 8.Kopans, D.B.: Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 94(2), 580–581 (2002)CrossRefGoogle Scholar
- 9.Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)Google Scholar
- 10.Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 4165 (2018)CrossRefGoogle Scholar
- 11.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
- 12.Wang, N., et al.: Densely deep supervised networks with threshold loss for cancer detection in automated breast ultrasound. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 641–648. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_73CrossRefGoogle Scholar
- 13.Wu, N., et al.: Breast density classification with deep convolutional neural networks. In: ICASSP (2018)Google Scholar
- 14.Wu, N., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. arXiv preprint arXiv:1903.08297 (2019)
- 15.Yao, L., Prosky, J., Poblenz, E., Covington, B., Lyman, K.: Weakly supervised medical diagnosis and localization from multiple resolutions. arXiv:1803.07703 (2018)
- 16.Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 603–611. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_69CrossRefGoogle Scholar