Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
This addresses the issue of high false-positive biopsy rates for women undergoing mammography, though it is incremental as it builds on existing deep learning methods.
The study tackled the problem of reducing unnecessary biopsies in breast cancer screening by developing deep neural networks to classify lesions as malignant or benign, achieving an AUC of 0.8 on a test set with 464 benign and 136 malignant lesions.
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.