← Back to all news
arXiv Machine Learning By Mahshad Lotfinia, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh

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

Read the original on arXiv Machine Learning →

The paper introduces FRAME, a two‑step framework for auditing fairness claims in medical imaging. First, it derives a fair‑model reference distribution that captures the portion of subgroup performance differences attributable to sampling variation. Second, it tests the remaining difference using operators in representation space to assess whether demographic information or disease entanglement drives the bias. Across a large dataset of 702,206 images and 36 encoders, the reference explains a substantial median share of race and age differences, while interventions such as injecting demographic decodability or entangling disease direction have limited impact on the residual bias.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 17

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.

By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier
diffusionfine-tuningsafety
More like this →
arXiv AI
Aug 20

Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening

The paper introduces the Explanation Consistency Score (ECS), a fairness‑aware metric that uses Jensen‑Shannon divergence to measure how similar attribution maps are across demographic subgroups. Applied to diabetic retinopathy screening, ECS is evaluated both overall and within disease severity levels. Results show that although predictive performance varies among ethnic groups, explanation consistency remains high and is not significantly linked to performance disparities, indicating that predictive fairness and explanation consistency assess different aspects of model behavior.

By Kerol Djoumessi, Philipp Berens
safety
More like this →