Corpus ID: 221818645

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

@article{Wu2020ReducingFB,
  title={Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms},
  author={Nan Wu and Z. Huang and Yiqiu Shen and Jungkyu Park and Jason Phang and T. Makino and S. Kim and Kyunghyun Cho and Laura Heacock and L. Moy and Krzysztof J Geras},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.09282}
}

Nan Wu, Z. Huang, +8 authors Krzysztof J Geras

Published 2020

Computer Science, Engineering

ArXiv

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… CONTINUE READING

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