构建可解释的视网膜图像质量控制框架，提升糖尿病视网膜病变检测数据可信度
Explainable Fundus Image Curation and Lesion Detection in Diabetic Retinopathy
- 结合图像处理与对比学习提取特征，用可解释分类器筛选低质图像
- 通过深度学习辅助标注并计算标注者一致性，确保数据可用性
- 适用于医疗AI训练数据清洗，尤其适合眼科疾病研究团队
糖尿病视网膜病变（DR）是长期糖尿病患者的常见并发症，早期诊断可避免视力丧失。眼底摄影能捕捉视网膜结构及病变特征，反映疾病阶段。人工智能（AI）可辅助临床识别病灶，降低人工负担，但依赖高质量标注数据。由于视网膜结构复杂，图像采集与人工标注易出错。本文提出一种可解释的质量控制框架，确保仅使用高标准数据进行评估与AI训练。首先，采用基于特征的可解释分类器筛选不合格图像，特征由图像处理与对比学习共同提取；其次，对图像进行增强并借助深度学习辅助标注；最后，通过推导公式计算标注者间一致性，判定标注结果是否可用。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) affects individuals with long-term diabetes. Without early diagnosis, DR can lead to vision loss. Fundus photography captures the structure of the retina along with abnormalities indicative of the stage of the disease. Artificial Intelligence (AI) can support clinicians in identifying these lesions, reducing manual workload, but models require high-quality annotated datasets. Due to the complexity of retinal structures, errors in image acquisition and lesion interpretation of manual annotators can occur. We proposed a quality-control framework, ensuring only high-standard data is used for evaluation and AI training. First, an explainable feature-based classifier is used to filter inadequate images. The features are extracted both using image processing and contrastive learning. Then, the images are enhanced and put subject to annotation, using deep-learning-based assistance. Lastly, the agreement between annotators calculated using derived formulas determines the usability of the annotations.
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