Edwin D. de Jong
@edwinddejong.bsky.social
Medical AI Researcher at Aignostics
https://edwin-de-jong.github.io/
https://edwin-de-jong.github.io/
Pathology Foundation models (FMs)
more strongly encode medical center info than biological info such as cancer type. The Robustness Metric is introduced to evaluate this. We hope this new tool can advance progress towards robust, reliable pathology FMs. arxiv.org/abs/2501.18055
more strongly encode medical center info than biological info such as cancer type. The Robustness Metric is introduced to evaluate this. We hope this new tool can advance progress towards robust, reliable pathology FMs. arxiv.org/abs/2501.18055
Current Pathology Foundation Models are unrobust to Medical Center Differences
Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We m...
arxiv.org
January 31, 2025 at 5:43 AM
Pathology Foundation models (FMs)
more strongly encode medical center info than biological info such as cancer type. The Robustness Metric is introduced to evaluate this. We hope this new tool can advance progress towards robust, reliable pathology FMs. arxiv.org/abs/2501.18055
more strongly encode medical center info than biological info such as cancer type. The Robustness Metric is introduced to evaluate this. We hope this new tool can advance progress towards robust, reliable pathology FMs. arxiv.org/abs/2501.18055