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FRAME: separating sampling variation from representational cause in medical imaging fairness

Research Medical/Healthcare AI

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TL;DR - FRAME is a two-step framework for determining whether subgroup performance gaps in medical imaging reflect sampling variation or representational mechanisms. Across large-scale experiments and prior studies, it attributes substantial portions of reported fairness gaps to cohort-size effects and questions interventions focused only on removing demographic information.

  • FRAME first estimates the expected performance-gap distribution under exact fairness at observed subgroup sizes, then tests the remaining gap with representation-space interventions.
  • Across 702,206 images and 36 encoders, sampling variation explained a median 41% of race differences and 22% of age differences.
  • Adding demographic decodability did not change the remainder, while entangling demographic group with the disease direction increased the race difference from 0.077 to 0.118.
  • Across 89 differences from nine studies, the reference explained a median 25% of rate gaps and 70% of AUROC gaps; image-text pretraining improved worst-group performance by about 0.05.

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FRAME: separating sampling variation from representational cause in medical imaging fairness

arXiv cs.CV Mahshad Lotfinia, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh 2026-08-26 arXiv:2608.25981
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-30 14:11:02.091225 UTC

TL;DR - FRAME is a two-step framework for determining whether subgroup performance gaps in medical imaging reflect sampling variation or representational mechanisms. Across large-scale experiments and prior studies, it attributes substantial portions of reported fairness gaps to cohort-size effects and questions interventions focused only on removing demographic information.

  • FRAME first estimates the expected performance-gap distribution under exact fairness at observed subgroup sizes, then tests the remaining gap with representation-space interventions.
  • Across 702,206 images and 36 encoders, sampling variation explained a median 41% of race differences and 22% of age differences.
  • Adding demographic decodability did not change the remainder, while entangling demographic group with the disease direction increased the race difference from 0.077 to 0.118.
  • Across 89 differences from nine studies, the reference explained a median 25% of rate gaps and 70% of AUROC gaps; image-text pretraining improved worst-group performance by about 0.05.
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