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MedVision addresses a critical gap in medical AI by benchmarking vision-language models on quantitative tasks like tumor size estimation and angle measurement, areas where current off-the-shelf models underperform. The dataset, comprising over 30 million image-annotation pairs across 22 sources, demonstrates that supervised and reinforcement fine-tuning significantly improve performance in these clinically vital assessments. This resource provides a necessary foundation for developing VLMs capable of the precise numerical reasoning required for robust clinical decision-making.
MedVision: Benchmarking Quantitative Medical Image Analysis
arXiv:2511.18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks. However, clinical decision-making often relies on quantitative assessments, such as measuring the size of a tumor or the angle of a joint, from which physicians draw their own diagnostic conclusions. This quantitative reasoning capability remains underexplored and poorly supported in existing VLMs. In this work, we introduce MedVision, a large-scale dataset and benchmark specifically designed to evaluate and improve VLMs on quantitative medical image analysis. MedVision spans 22 public datasets covering diverse anatomies and modalities, with 30.8 million image-annotation pairs. We focus on three representative quantitative tasks: (1) detection of anatomical structures and abnormalities, (2) tumor/lesion (T/L) size estimation, and (3) angle/distance (A/D) measurement. We show that current off-the-shelf VLMs perform poorly on these tasks. However, supervised and reinforcement fine-tuning on MedVision significantly enhances performance across detection, T/L estimation, and A/D measurement. MedVision provides a foundation for developing VLMs with robust quantitative reasoning capabilities in medical imaging.
Abstract:Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks. However, clinical decision-making often relies on quantitative assessments, such as measuring the size of a tumor or the angle of a joint, from which physicians draw their own diagnostic conclusions. This quantitative reasoning capability remains underexplored and poorly supported in existing VLMs. In this work, we introduce MedVision, a large-scale dataset and benchmark specifically designed to evaluate and improve VLMs on quantitative medical image analysis. MedVision spans 22 public datasets covering diverse anatomies and modalities, with 30.8 million image-annotation pairs. We focus on three representative quantitative tasks: (1) detection of anatomical structures and abnormalities, (2) tumor/lesion (T/L) size estimation, and (3) angle/distance (A/D) measurement. We show that current off-the-shelf VLMs perform poorly on these tasks. However, supervised and reinforcement fine-tuning on MedVision significantly enhances performance across detection, T/L estimation, and A/D measurement. MedVision provides a foundation for developing VLMs with robust quantitative reasoning capabilities in medical imaging.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2511.18676 [cs.CV]
(or arXiv:2511.18676v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2511.18676 Focus to learn more arXiv-issued DOI via DataCite
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