提出8项临床标准评估眼底图像质量，提升糖尿病视网膜病变筛查可靠性。
FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
- 基于8个关键参数构建可量化眼底图像质量的评估框架
- 模型在1800张真实临床图像上训练，预测得分准确率达0.92（皮尔逊相关）
- 适用于糖尿病视网膜病变诊断等真实医疗场景的质量感知训练
由于成像差异和专家评价主观性，自动化眼底图像质量评估仍具挑战。本文提出FundaQ-8，一种经专家验证的系统化评分框架，涵盖视野覆盖、解剖结构可见性、光照条件及图像伪影等8个核心参数。以FundaQ-8为标注参考，构建基于ResNet18的回归模型，输出0至1之间的连续质量分数。模型在1800张来自真实临床与Kaggle数据集的图像上通过迁移学习、均方误差优化与标准化预处理训练。在EyeQ数据集上的验证及统计分析表明该框架具有可靠性和临床可解释性。将FundaQ-8引入糖尿病视网膜病变分级模型训练后，显著提升诊断鲁棒性，证明质量感知训练在真实筛查中的价值。
原文摘要 · Abstract (English)
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.
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