用眼底图和病变图融合提升糖尿病视网膜病变分类准确率
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification
- 通过Swin U-Net生成病变图，与原眼底图联合输入
- 跨注意力机制使模型识别细微病变，准确率达94.6%
- 适合临床辅助诊断系统，提升筛查效率
糖尿病视网膜病变（DR）是导致失明的主要原因，需尽早发现。本文聚焦可干预性DR分类，提出一种基于迁移学习与跨注意力机制的先进分类方法。利用Swin U-Net从眼底图像中分割出病变图，该模型融合了DR病变先验知识。将原始眼底图与分割出的病变图作为互补输入，采用跨注意力机制增强模型对细粒度特征的捕捉能力。在两个公开数据集FGADR和EyePACS上的实验表明，该方法准确率达到94.6%，比现有最优方法高出4.4%。研究旨在推动该方法无缝融入临床流程，提升可干预性DR的识别准确率与效率。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model's ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR.
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