End-to-End Multi-task Learning Regression Network for Fovea Localization in Fundus Images

Abstract

Macular fovea localization in fundus images is a critical stage for computer-aided diagnostic techniques of many retinal diseases. Due to its cluttered visual characteristics, it is difficult to accurately locate the fovea. Many previous methods obtain the location of macular fovea from pre-extracting image features extracted from surrounding structures, such as optic disc and vascular distribution. Deep learning-based regression techniques are promising due to their effective modeling of the relationship between the fovea and its surrounding structure for fovea localization. However, there are still many challenges to locate the fovea using deep learning accurately. To address these issues, we design a novel end-to-end multi-task learning regression network for fovea localization. Specifically, the proposed network consists of two regression networks. For the coordinate regression network, we introduce multi-scale fusion technology and a multi-head self-attention module to extract discriminative context information and capture long-term dependence, respectively. For the heatmap regression network, the generated heatmap according to the coordinates is utilized to supervise the output of the network. The experimental results on three public datasets demonstrate that our method achieves superior performance for the localization of macular fovea.