LHCF trains medical image models for fairness by optimizing across latent appearance-based cohorts discovered via clustering, achieving SOTA results on single and multiple demographic attributes without using any demographic labels.
arXiv preprint arXiv:2203.14960 (2022)
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SliceScorer combines an exposure-based coverage prior and a neighbor-failure prior into a simple deterministic score for recommending coverage gaps in driving VLMs, embedded in the LLM-orchestrated SliceNav pipeline.
citing papers explorer
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Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging
LHCF trains medical image models for fairness by optimizing across latent appearance-based cohorts discovered via clustering, achieving SOTA results on single and multiple demographic attributes without using any demographic labels.
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What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs
SliceScorer combines an exposure-based coverage prior and a neighbor-failure prior into a simple deterministic score for recommending coverage gaps in driving VLMs, embedded in the LLM-orchestrated SliceNav pipeline.
- Intersectional Disentangling of Temporal and Acquisition Bias in Fetal Ultrasound