Multiple ocular diseases detection based on joint sparse multi-task learning.

Abstract

In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.