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MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Reliable evaluation of vision-language models (VLMs) and medical vision-language models (Medical-VLMs) requires calibrated confidence, particularly under realistic clinical conditions. However, existing efforts mainly focused on improving accuracy, leaving calibration in the medical domain underexplored. To this end, we propose MVC-Bench, a calibration-centric benchmark for medical image classification with VLMs and Medical-VLMs. MVC-Bench assesses the calibration across three axes: (i) robustness to modality, backbone, and domain shift (ii) effectiveness of calibration strategies and prompt-t
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T11:49:46.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.