用注意力机制提升糖尿病视网膜病变病灶分割精度，助力早期筛查。
Diabetic Retinopathy Lesion Segmentation through Attention Mechanisms
- 在DeepLab-V3+中引入注意力机制，聚焦细微病灶区域。
- 微动脉瘤检测率从2.05%提升至7.63%，显著改善早期诊断能力。
- 适用于眼科医生快速筛查，临床价值高。
糖尿病视网膜病变（DR）是糖尿病引发的眼部疾病，可能导致失明。早期筛查对防止不可逆视力损伤至关重要。尽管已有大量深度学习算法用于自动筛查，但其在病灶分割上的临床应用仍受限。本文基于DDR数据集中的757张眼底图像，对四类DR相关病灶——微动脉瘤、软渗出、硬渗出和出血——进行像素级分割，以支持眼科医生筛查。通过在DeepLab-V3+中集成注意力机制，构建Attention-DeepLab模型。相较于基线模型，该模型的平均精确率（mAP）从0.3010提升至0.3326，平均交并比（IoU）从0.1791增至0.1928，微动脉瘤检测率由0.0205提升至0.0763，实现临床显著改进。微动脉瘤是DR最早可见症状，其检测能力提升具有重要意义。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is an eye disease which arises due to diabetes mellitus. It might cause vision loss and blindness. To prevent irreversible vision loss, early detection through systematic screening is crucial. Although researchers have developed numerous automated deep learning-based algorithms for DR screening, their clinical applicability remains limited, particularly in lesion segmentation. Our method provides pixel-level annotations for lesions, which practically supports Ophthalmologist to screen DR from fundus images. In this work, we segmented four types of DR-related lesions: microaneurysms, soft exudates, hard exudates, and hemorrhages on 757 images from DDR dataset. To enhance lesion segmentation, an attention mechanism was integrated with DeepLab-V3+. Compared to the baseline model, the Attention-DeepLab model increases mean average precision (mAP) from 0.3010 to 0.3326 and the mean Intersection over Union (IoU) from 0.1791 to 0.1928. The model also increased microaneurysm detection from 0.0205 to 0.0763, a clinically significant improvement. The detection of microaneurysms is the earliest visible symptom of DR.
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