APNet: Adaptive Patch-based Network for Microaneurysm Detection in Fundus Images

Microaneurysms (MAs) in fundus images are a crucial indicator for the early detection of diabetes and the treatment of diabetic retinopathy. In this paper, we propose an efficient method for microaneurysms detection. We propose an Att-UCtransnet network to extract the MA candidate region, where only positive samples which contain at least one microaneurysm are chosen. Inspired by the concept of the effective receptive field (ERF), we design an algorithm to adaptively select the appropriate patch size, preserving crucial information around the microaneurysm while minimizing interference from irrelevant environments. Besides, we introduce a classification network for accurate mass detection in whole fundus images. Extensive experiments conducted on four publicly available datasets demonstrate that our method outperforms the state-of-the-art approaches in MA detection.