ArticleJournal of digital imaging2021
Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Application of a Commercial Artificial Intelligence Software in Unilateral Mammography: Simulating Total Mastectomy Scenarios.Journal of imaging informatics in medicine · 2025Article
- Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography.PLOS digital health · 2025Article
- A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.Radiology. Artificial intelligence · 2024Article
- Differences between human and machine perception in medical diagnosis.Scientific reports · 2022Article
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Authors and funding
11 authors.
Funding
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 examinations 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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