2018/11/23 by Sudharshan Chandra Babu, Babu, Sudharshan Chandra, Shishira R Maiya +3 · 1 citation
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Glaucoma and retinal disorders #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.1812.00883
openalex publication_date 2018/11/23 · openalex created_date 2018/12/11 · openalex updated_date 2026/07/28
Diabetic Retinopathy is the leading cause of blindness in the world. At least 90% of new cases can be reduced with proper treatment and monitoring of the eyes. However, scanning the entire population of patients is a difficult endeavor. Computer-aided diagnosis tools in retinal image analysis can make the process scalable and efficient. In this work, we focus on the problem of localizing the centers of the Optic disc and Fovea, a task crucial to the analysis of retinal scans. Current systems recognize the Optic disc and Fovea individually, without exploiting their relations during learning. We propose a novel approach to localizing the centers of the Optic disc and Fovea by simultaneously processing them and modeling their relative geometry and appearance. We show that our approach improves localization and recognition by incorporating object-object relations efficiently, and achieves highly competitive results.