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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)
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)
Diba, A., Sharma, V., Pazandeh, A.M., Pirsiavash, H., Van Gool, L.: Weakly supervised cascaded convolutional networks. In: CVPR (2017)
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)
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)
Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv:1802.04712 (2018)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Kopans, D.B.: Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 94(2), 580–581 (2002)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
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)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
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_73
Wu, N., et al.: Breast density classification with deep convolutional neural networks. In: ICASSP (2018)
Wu, N., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. arXiv preprint arXiv:1903.08297 (2019)
Yao, L., Prosky, J., Poblenz, E., Covington, B., Lyman, K.: Weakly supervised medical diagnosis and localization from multiple resolutions. arXiv:1803.07703 (2018)
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_69
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, Y. et al. (2019). Globally-Aware Multiple Instance Classifier for Breast Cancer Screening. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)