Abstract
Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores in the 0 to 1 range. The model is trained on 1800 fundus images from real-world clinical sources and Kaggle datasets, using transfer learning, mean squared error optimization, and standardized preprocessing. Validation against the EyeQ dataset and statistical analyses confirm the framework's reliability and clinical interpretability. Incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading also improves diagnostic robustness, highlighting the value of quality-aware training in real-world screening applications.
Abstract (translated)
自动化视网膜图像质量评估（FIQA）由于成像获取的差异和主观专家评价的存在，仍然是一项挑战。我们引入了FundaQ-8，这是一个经过专家验证的新框架，用于系统地评估视网膜图像的质量，采用了八个关键参数，包括视野覆盖范围、解剖结构可见度、照明情况以及图像伪影等。 利用FundaQ-8作为结构化的评分参考，我们开发了一个基于ResNet18的回归模型来预测0到1范围内连续的质量分数。该模型在来自实际临床来源和Kaggle数据集的1800张视网膜图像上进行训练，采用了迁移学习、均方误差优化以及标准化预处理技术。 通过对EyeQ数据集进行验证并结合统计分析，证实了此框架具备可靠性和临床解释性。将FundaQ-8融入用于糖尿病视网膜病变分级的深度学习模型中，也进一步提高了诊断的稳健性，这突显出在实际筛查应用中质量感知训练的价值。
URL
https://arxiv.org/abs/2506.20303
