MedVision Logo MedVision: Benchmarking Quantitative Medical Image Analysis

Yongcheng Yao1 , Yongshuo Zong1 , Raman Dutt1 , Yongxin Yang3 ,
Sotirios A Tsaftaris2 , Timothy Hospedales1
1School of Informatics, University of Edinburgh
2School of Engineering, University of Edinburgh
3Queen Mary University of London
arXiv GitHub Docs Data Models Demo Project Data Explorer

MedVision is a dataset and benchmark for quantitative medical image analysis, including detection, tumor/lesion (T/L) size estimation, and angle/distance (A/D) measurement tasks.

📝 Blog

Stories from behind the dataset: what changed, why it changed, and what it means for the people who use MedVision. Written for everyone, not just researchers.

  1. Measuring What Matters: 50× More Tumor & Lesion Measurements Dataset v1.4.0 rebuilds how MedVision measures every tumor and lesion, choosing what to measure by real physical size instead of pixel counts, and grows the measurements from 76 thousand to 3.8 million. Read post

Acknowledgements

This work was supported by the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics; the Edinburgh International Data Facility (EIDF) and the Data-Driven Innovation Programme at the University of Edinburgh.

BibTeX

@misc{yao2026medvisionbenchmarkingquantitativemedical,
    title={MedVision: Benchmarking Quantitative Medical Image Analysis}, 
    author={Yongcheng Yao and Yongshuo Zong and Raman Dutt and Yongxin Yang and Sotirios A Tsaftaris and Timothy Hospedales},
    year={2026},
    eprint={2511.18676},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2511.18676}, 
}