Abstract
Accurate medical image analysis can greatly assist clinical diagnosis, but its effectiveness relies on high-quality expert annotations Obtaining pixel-level labels for medical images, particularly fundus images, remains costly and time-consuming. Meanwhile, despite the success of deep learning in medical imaging, the lack of interpretability limits its clinical adoption. To address these challenges, we propose TWLR, a two-stage framework for interpretable diabetic retinopathy (DR) assessment. In the first stage, a vision-language model integrates domain-specific ophthalmological knowledge into text embeddings to jointly perform DR grading and lesion classification, effectively linking semantic medical concepts with visual features. The second stage introduces an iterative severity regression framework based on weakly-supervised semantic segmentation. Lesion saliency maps generated through iterative refinement direct a progressive inpainting mechanism that systematically eliminates pathological features, effectively downgrading disease severity toward healthier fundus appearances. Critically, this severity regression approach achieves dual benefits: accurate lesion localization without pixel-level supervision and providing an interpretable visualization of disease-to-healthy transformations. Experimental results on the FGADR, DDR, and a private dataset demonstrate that TWLR achieves competitive performance in both DR classification and lesion segmentation, offering a more explainable and annotation-efficient solution for automated retinal image analysis.
Abstract (translated)
准确的医学影像分析能够极大地辅助临床诊断，但其有效性依赖于高质量的专业标注。获取医学图像（尤其是眼底图像）的像素级标签既耗时又昂贵。尽管深度学习在医学成像领域的成功已经取得突破，但缺乏可解释性限制了其临床应用。为了解决这些问题，我们提出了TWLR，这是一种用于可解释的糖尿病视网膜病变（DR）评估的两阶段框架。 第一阶段中，视觉-语言模型整合特定领域的专业眼科知识到文本嵌入中，从而同时执行DR分级和病变分类。这有效地将语义医学概念与视觉特征联系起来。 第二阶段引入了一个基于弱监督语义分割的迭代严重程度回归框架。通过迭代细化生成的病变显著图引导渐进式填充机制，系统地消除病理特征，使疾病严重程度逐步向更健康的眼底外观退化。关键在于这一严重度回归方法实现了双重效果：在没有像素级监督的情况下实现准确的病变定位，并提供可解释的疾病到健康转变可视化。 实验结果表明，在FGADR、DDR以及一个私有数据集上，TWLR在DR分类和病变分割方面均达到了竞争性的性能，为自动视网膜图像分析提供了更加可解释且标注效率更高的解决方案。
URL
https://arxiv.org/abs/2512.13008
