Diabetic retinopathy (DR) is a leading global cause of blindness. Early detection of hard exudates plays a crucial role in identifying DR, which aids in treating diabetes and preventing vision loss. However, the unique characteristics of hard exudates, ranging from their inconsistent shapes to indistinct boundaries, pose significant challenges to existing segmentation techniques. To address these issues, we present a novel supervised contrastive learning framework to optimize hard exudate segmentation. Specifically, we introduce a patch-wise density contrasting scheme to distinguish between areas with varying lesion concentrations, and therefore improve the model's proficiency in segmenting small lesions. To handle the ambiguous boundaries, we develop a discriminative edge inspection module to dynamically analyze the pixels that lie around the boundaries and accurately delineate the exudates. Upon evaluation using the IDRiD dataset and comparison with state-of-the-art frameworks, our method exhibits its effectiveness and shows potential for computer-assisted hard exudate detection. The code to replicate experiments is available at github.com/wetang7/HECL/.

翻译：糖尿病视网膜病变是全球范围内导致失明的主要原因之一。硬性渗出的早期检测对于识别该病变至关重要，有助于治疗糖尿病并预防视力丧失。然而，硬性渗出在形状不规则和边界模糊等独特特性给现有分割技术带来了显著挑战。为解决这些问题，我们提出了一种新颖的监督对比学习框架，以优化硬性渗出分割。具体而言，我们引入了一种分块密度对比方案，以区分病变浓度不同的区域，从而提升模型对小病灶的分割能力。针对边界模糊问题，我们开发了一个判别性边缘检测模块，动态分析边界周围的像素，以精确勾勒渗出区域。通过在IDRiD数据集上的评估以及与现有最优框架的比较，我们的方法展现了其在辅助硬性渗出检测中的有效性与潜力。实验复现代码可从github.com/wetang7/HECL/获取。