Abstract
The retinal fundus images are extensively utilized in diagnosis, and their quality may affect diagnostic results. However, due to limitations in the datasets and algorithms, current fundus image quality assessment (FIQA) methods often lack the granularity required to meet clinical demands. To address these limitations, we introduce a new benchmark FIQA dataset, Fundus Quality Score, which contains 2,246 images annotated with continuous mean opinion scores ranging from 0 to 100 and three-level quality categories. Meanwhile, we also design a novel FIQA Transformer-based Hypernetwork (FTHNet). The FTHNet can treat FIQA as a regression task to predict the continuous MOS, diverging from common classification-based approaches. Results on our dataset show that FTHNet predicts quality scores, achieving a Pearson Linear Correlation Coefficient of 0.9423 and a Spearman Rank Correlation Coefficient of 0.9488, significantly outperforming compared methods while utilizing fewer parameters and lower computational complexity. Furthermore, model deployment experiments demonstrate its potential for use in automated medical image quality control workflows. We have released the code and dataset to facilitate future research in this field.