Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms

• URL: http://arxiv.org/abs/2009.09282v1
• Date: Sat, 19 Sep 2020 18:54:01 GMT
• Title: Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
• Authors: Nan Wu and Zhe Huang and Yiqiu Shen and Jungkyu Park and Jason Phang and Taro Makino and S. Gene Kim and Kyunghyun Cho and Laura Heacock and Linda Moy and Krzysztof J. Geras
• Abstract summary: It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign.
• Score: 45.19322938294639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography exams from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.

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