EurekAlert! Cancer Research

Updates every hour. Last Updated: 12-Sep-2025 13:11 ET (12-Sep-2025 17:11 GMT/UTC)

6-May-2025

Artificial intelligence-based diagnosis of breast cancer by mammography microcalcification

KeAi Communications Co., Ltd.
Peer-Reviewed Publication

This study introduces a deep-learning system for rapid, automated detection and classification of tiny calcium deposits (microcalcifications) in mammograms to aid early breast cancer diagnosis. Leveraging a multi-center dataset of 4,810 biopsy-confirmed mammograms, our pipeline uses a Faster RCNN model with a feature-pyramid backbone to detect and classify microcalcifications—the pipeline requires no hand-tuned rules and provides both the overall cancer risk and highlighted lesion regions in seconds per image. On unseen test data, it achieved overall classification accuracy of 72% for discriminating between benign and malignant breasts and 78% sensitivity of malignant breast cancer prediction, marking a significant step toward AI-assisted, cost-effective breast-cancer screening that can run on standard radiology workstations.

Journal: Fundamental Research
Funder: National Natural Science Foundation of China, the 2018 Shanghai Youth Excellent Academic Leader, the Fudan ZHUOSHI Project, Chinese Young Breast Experts Research project, Shanghai Engineering Research Center of Artificial Intelligence Technology for Tumor Diseases, Xuhui District Artificial Intelligence Medical Hospital Cooperation Project, Shanghai Science and Technology Foundation, Clinical Research Plan of SHDC, Natural Science Foundation of Shanghai

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