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MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models

Research Medical/Healthcare AI

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TL;DR - MVC-Bench evaluates confidence calibration in medical vision-language models across clinical imaging modalities, distribution shifts, and prompt variations. It also introduces Multi-Class Margin regularization, which substantially improves calibration across the tested settings.

  • Covers eight model backbones and three modalities: fundus imaging, histopathology, and chest X-rays.
  • Compares post-hoc, train-time, zero-shot, and six prompt-tuning approaches across more than 1,638 controlled experiments.
  • Measures accuracy alongside Expected, Maximum, and Adaptive Calibration Error, including under domain shifts and prompt or seed variations.
  • Multi-Class Margin regularization achieves the lowest Expected Calibration Error in 10 of 12 in-domain settings and remains competitive under domain shift.

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MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models

arXiv cs.CV Ashshak Sharifdeen, Shihab Aaqil Ahamed, Ufaq Khan, Muhammad Akhtar Munir Sujair Ibrahim, Mohamed Rafeek Mareer Ahamed, Yutong Xie, Imran Razzak, Muhammad Haris Khan 2026-08-27 arXiv:2608.27004
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 2026-09-24 14:31:22.421162 UTC

TL;DR - MVC-Bench evaluates confidence calibration in medical vision-language models across clinical imaging modalities, distribution shifts, and prompt variations. It also introduces Multi-Class Margin regularization, which substantially improves calibration across the tested settings.

  • Covers eight model backbones and three modalities: fundus imaging, histopathology, and chest X-rays.
  • Compares post-hoc, train-time, zero-shot, and six prompt-tuning approaches across more than 1,638 controlled experiments.
  • Measures accuracy alongside Expected, Maximum, and Adaptive Calibration Error, including under domain shifts and prompt or seed variations.
  • Multi-Class Margin regularization achieves the lowest Expected Calibration Error in 10 of 12 in-domain settings and remains competitive under domain shift.
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