Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report
This work addresses computer-aided diagnosis of AMD, a major cause of blindness in the elderly, but appears incremental as it builds on existing deep networks and GANs.
The authors tackled the problem of diagnosing age-related macular degeneration (AMD) using color fundus images, proposing deep learning methods including a classification pipeline and novel GAN-based approaches for tasks like lesion segmentation and fovea detection, but no concrete performance numbers were provided.
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.