Clustering patch embeddings from a pre-trained breast cancer risk model identifies recurring phenotypes correlated with 5-year risk, including dense tissue, microcalcifications, and shortcut artifacts.
Mammographic Density and the Risk and Detection of Breast Cancer | New England Journal of Medicine
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A workflow generates consistent dense tissue masks in DBT volumes by annotating only the central slice, projecting the ROI, and iteratively adjusting per-slice thresholds, yielding median Dice scores of 0.83 against manual segmentations.
citing papers explorer
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Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
Clustering patch embeddings from a pre-trained breast cancer risk model identifies recurring phenotypes correlated with 5-year risk, including dense tissue, microcalcifications, and shortcut artifacts.
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A Workflow to Efficiently Generate Dense Tissue Ground Truth Masks for Digital Breast Tomosynthesis
A workflow generates consistent dense tissue masks in DBT volumes by annotating only the central slice, projecting the ROI, and iteratively adjusting per-slice thresholds, yielding median Dice scores of 0.83 against manual segmentations.