Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical IQA methods, however, struggle to generalize across diverse modalities and clinical scenarios. In response, we introduce MedIQA, the first comprehensive foundation model for medical IQA, designed to handle variability in image dimensions, modalities, anatomical regions, and types. We developed a large-scale multi-modality dataset with plentiful manually annotated quality scores to support this. Our model integrates a salient slice assessment module to focus on diagnostically relevant regions feature retrieval and employs an automatic prompt strategy that aligns upstream physical parameter pre-training with downstream expert annotation fine-tuning. Extensive experiments demonstrate that MedIQA significantly outperforms baselines in multiple downstream tasks, establishing a scalable framework for medical IQA and advancing diagnostic workflows and clinical decision-making.
@article{arxiv.2507.19004,
title = {MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment},
author = {Siyi Xun and Yue Sun and Jingkun Chen and Zitong Yu and Tong Tong and Xiaohong Liu and Mingxiang Wu and Tao Tan},
journal= {arXiv preprint arXiv:2507.19004},
year = {2025}
}
Comments
We note that the version after peer review of this paper has been provisionally accepted by The 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)