  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/.
