Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We benchmark 15 foundation-model backbones across breast density, BI-RADS severity, and cancer status using a unified frozen-backbone linear-probe protocol, training on 3 source datasets and evaluating on 12 task-compatible out-of-distribution (OOD) datasets after label harmonization. Mammography-specific vision-language models (Mammo-FM and MaMA) provide the strongest mean OOD performance, but robustness is not explained by mammography exposure alone. DINOv3 remains a competitive vision-only baseline, and mammography-adapted pretraining do...
| Authors | Giang Nguyen, Raghav Mehta, Emma A. M. Stanley et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 July 2026 |
| Last updated at source | 27 August 2026 |
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