BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization

• URL: http://arxiv.org/abs/2608.10271v1
• Date: Mon, 10 Aug 2026 22:10:13 GMT
• Title: BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
• Abstract summary: We introduce two new datasets, BreastMammo and DenseMammo, to facilitate robust multi-view mammography research.<n>We propose a domain generalization framework that utilizes a foreground-only histogram matching protocol to resolve the domain shift issue arising from disparate clinical sources.
• Score: 12.858068155615667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acquisition styles. In this work, we introduce two new datasets, BreastMammo and DenseMammo, to facilitate robust multi-view mammography research. We propose a domain generalization framework that utilizes a foreground-only histogram matching protocol to resolve the domain shift issue arising from disparate clinical sources. Internal evaluation using a 5-fold cross-validation protocol demonstrates the efficacy of our approach, with the Swin Transformer backbone achieving a peak AUC of 98.32% for density classification. External evaluation on the TNMammo and LUMINA datasets demonstrates that the proposed approach consistently reduces domain shift, significantly outperforming prominent domain generalization paradigms, including MixStyle and Discrete-Fourier-Transform-based frameworks.

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