BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
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 classificati...
| Authors | Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas et al. |
|---|---|
| Primary category | eess.IV |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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