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About this paper
Siyi Xun, Yue Sun, Jingkun Chen, Zitong Yu, Tong Tong, Xiaohong Liu, Mingxiang Wu, Tao Tan
MedIQA is introduced as a prompt-driven foundation model for medical image quality assessment (IQA), developed alongside a large-scale, multi-modal dataset for training. The model demonstrates superior performance across diverse medical imaging tasks, achieving average SRCC of 0.8422, PLCC of 0.8668, and RMSE of 0.1546 on downstream tasks, which marks a 7.79% SRCC and 7.88% PLCC improvement over average baseline results.
Key insights
- Pre-training on objective physical parameters establishes an explicit and interpretable link between physical image characteristics and perceived quality, providing a strong foundation for subjective assessment.
- The salient slice assessment module significantly enhances efficiency and accuracy in 3D medical image quality assessment by focusing computational resources on critical diagnostic regions.
- An automated prompt strategy, encoding image metadata (dimension, modality, region, type), enables the foundation model to dynamically adapt and generalize effectively across diverse IQA tasks.
Summary by alphaXiv.
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