Each year, millions of women undergo mammography to screen for breast cancer, yet tiny calcium specks—known as microcalcifications—often evade detection or are misread, leading to delayed diagnoses or unnecessary biopsies. Conventional computer-aided tools rely on hand-crafted rules and struggle with the sheer variety of imaging devices and lesion patterns.
In a recent study led by Dr. Ke-Da Yu from Fudan University Shanghai Cancer Center, a novel deep-learning approach that automatically finds and classifies microcalcifications across different machines and patient populations was developed—bringing both accuracy and consistency to breast-cancer screening.
“Microcalcifications can be just a few pixels wide. Hence, spotting them amid normal tissue is like finding a needle in a haystack,” explains Dr. Yu. “We wanted a system that adapts to any mammogram and never overlooks early warning signs.”
The team’s innovation rests on two key advances:
In blind testing, the pipeline processed each mammogram, achieving approximately 75% overall accuracy at the microcalcification-lesion level with 76% sensitivity for malignant lesions and about 72% accuracy at the breast level.
“This solution can be deployed directly on standard radiology workstations,” adds Dr. Yu. “By pre-marking suspicious regions on each mammogram, it enables radiologists to quickly focus on areas of concern, significantly reducing both missed diagnoses and unnecessary biopsies—thereby easing patient discomfort and lowering healthcare costs.”
The research team has open-sourced the code, and their next steps will focus on integrating the system into clinical workflows, with the aim of offering a reliable AI-driven tool for more widespread breast-cancer screening.
The study was published in Fundamental Research .
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Contact the author: Ke-Da Yu, Cancer Institute, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China, yukeda@fudan.edu.cn
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Fundamental Research
Imaging analysis
Human tissue samples
Artificial intelligence-based diagnosis of breast cancer by mammography microcalcification[J]
The authors declare that they have no conflicts of interest in this work.