Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report

TL;DR

This paper presents deep learning methods, including GANs, for comprehensive analysis of fundus images to aid in diagnosing age-related macular degeneration, covering classification, lesion detection, and segmentation.

Contribution

It introduces a unified deep learning pipeline for AMD diagnosis and auxiliary tasks, utilizing GANs for segmentation and fovea detection, advancing automated retinal analysis.

Findings

Effective AMD classification using deep networks

GAN-based segmentation and detection methods

Novel GAN approach for fovea detection

Abstract

Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for computer aided diagnosis of AMD. We leverage the recent state of the art deep networks for building a single fundus image based AMD classification pipeline. We also propose methods for the other directly relevant and auxiliary tasks such as lesions detection and segmentation, fovea detection and optic disc segmentation. We propose the use of generative adversarial networks (GANs) for the tasks of segmentation and detection. We also propose a novel method of fovea detection using GANs.