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

arXiv:2608.25981 · cs.CV, cs.AI, cs.LG · Submitted 2026-08-26 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-08-26

Updated: 2026-09-26

Code: https://github.com/mattgroh/fitzpatrick17k

Terminology

• AUROC — 160× in this paper · explained in 2 episodes
• Calibration — 21× in this paper · explained in 7 episodes
• Entanglement — 19× in this paper · explained in 4 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Fair-model Reference And Mechanism Evaluation — 4× in this paper
• Provenance — 4× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Differential Privacy — 3× in this paper · explained in 4 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Algorithmic Bias — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration Failure — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
• DINOv3
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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