Optic Disc Hemorrhage Detection via A Novel Position-Guided Attention Network with Small Samples on Fundus Images

Optic disc hemorrhage (ODH) is an important lesion factor for eye disease diagnosis. ODH has some specific displaying characteristics on fundus images, including covering fuzzy small domains and displaying similar to vessels, which greatly increase ODH detection difficulty. In this paper, we propose a novel position-guided attention network to detect optic disc hemorrhage on fundus images. Our method greatly takes advantage of the prior knowledge of ODH position information and the multitask learning dependencies related to the ODH segmentation and ODH classification, to build a position information attention module and a correlative feature fusion module. Moreover, we introduce a disc edge strength map on the optic disc to constrain the attention domains. Due to the limitation of ODH labeled data, an online hard case segmentation strategy on small samples are proposed to train our method. Experiments show that our method could greatly reduce the detection of false positives when detecting ODH on fundus images, so as to obtain superior performance on the ODH detection with small samples. Further, a fundus image dataset with professional ODH labels is published to advance the research of ODH detection (https://github.com/JieGenius/disc_hemo_dataset).