Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
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TL;DR - DITL is a mammography transfer-learning framework that adapts training to dataset-derived sample difficulty and neighborhood structure. It reports significant improvements across large breast-density and small lesion-classification datasets.
- Weights cross-entropy per sample using nearest-neighbor label purity in self-supervised feature space.
- Uses triplet supervision with a learnable margin to improve class separation and compactness.
- Requires no focal-loss-style hyperparameter tuning and adds negligible computational overhead.
- Achieves state-of-the-art VinDR-Mammo breast-density classification and significant gains on small ROI datasets (p < 0.0001).
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Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
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TL;DR - DITL is a mammography transfer-learning framework that adapts training to dataset-derived sample difficulty and neighborhood structure. It reports significant improvements across large breast-density and small lesion-classification datasets.
- Weights cross-entropy per sample using nearest-neighbor label purity in self-supervised feature space.
- Uses triplet supervision with a learnable margin to improve class separation and compactness.
- Requires no focal-loss-style hyperparameter tuning and adds negligible computational overhead.
- Achieves state-of-the-art VinDR-Mammo breast-density classification and significant gains on small ROI datasets (p < 0.0001).