Author

Mahshad Lotfinia

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Preprint Aug 2026

FRAME: Separating sampling variation from performance disparities in medical image analysis

Fair-model Reference And Mechanism Evaluation (FRAME) compares each reported difference with the difference expected from a perfectly fair model at the same subgroup counts and tests whether candidate causes of any excess over this fair-model reference change the difference when they are injected into model features.

Mahshad Lotfinia, D. Truhn, Andreas K. Maier et al. · 0 citations

#computer vision Jul 2026

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similar...

Soroosh Tayebi Arasteh, S. Ziegelmayer, Mahshad Lotfinia et al. · arXiv.org · 0 citations

#computer vision Preprint Aug 2026

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

Subgroup performance differences are the standard evidence for fairness bias in medical imaging, and the usual response removes the demographic information that a model encodes. Here we introduce Fair-model Reference And Mechanism Evaluation (FRAME), a two-step framework for auditing such a claim. The first step derive...

Mahshad Lotfinia, D. Truhn, Andreas K. Maier et al. · 0 citations