Artificial intelligence-based screening of breast cancer by mammography microcalcification accurate: Study
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:
• Adaptive, multi-scale detection: By integrating a faster region-based convolutional neural network (R-CNN) model with a feature-pyramid network (FPN), the pipeline fuses features at multiple resolutions-enabling it to localize both coarse clusters and individual specks without any manually tuned thresholds.
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