眼底(子宫)
计算机科学
域适应
人工智能
糖尿病性视网膜病变
分级(工程)
计算机视觉
模式识别(心理学)
眼科
医学
糖尿病
分类器(UML)
内分泌学
土木工程
工程类
作者
Jinye Ran,Guanghua Zhang,X. Fan,Ximei Zhang,Jian‐Wei Xie,Hao Zhang
标识
DOI:10.1016/j.compbiomed.2024.108418
摘要
Domain adaptation (DA) is commonly employed in diabetic retinopathy (DR) grading using unannotated fundus images, allowing knowledge transfer from labeled color fundus images. Existing DAs often struggle with domain disparities, hindering DR grading performance compared to clinical diagnosis. A source-free active domain adaptation method (SFADA), which generates features of color fundus images by noise, selects valuable ultra-wide-field (UWF) fundus images through local representation matching, and adapts models using DR lesion prototypes, is proposed to upgrade DR diagnostic accuracy. Importantly, SFADA enhances data security and patient privacy by excluding source domain data. It reduces image resolution and boosts model training speed by modeling DR grade relationships directly. Experiments show SFADA significantly improves DR grading performance, increasing accuracy by 20.90% and quadratic weighted kappa by 18.63% over baseline, reaching 85.36% and 92.38%, respectively. This suggests SFADA's promise for real clinical applications.
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