Multi-modality fusion learning for the automatic diagnosis of optic neuropathy

Abstract

Optic neuropathy is kind of common eye diseases, which usually causes irreversible vision loss. Early diagnosis is key to saving patients’ vision. Due to the similar early clinical manifestations of common optic neuropathy, it may cause misdiagnosis and delays in treatment. Worse, most diagnoses rely on experienced doctors. In this paper, we proposed a novel deep learning architecture GroupFusionNet (GFN) to diagnose five normal optic neuropathy diseases, including Anterior Ischemic Optic Neuropathy (AION), papilledema, papillitis, Optic Disc Vasculitis (ODV), and optic atrophy (OA). Specifically, we combined multi-modalities in clinic examination such as fundus image, visual field tests and age of each patient. GFN utilized two ResNet pathways to extract and fuse both features of fundus image and visual field tests, and the information of structured data was embedded in the end. Experimental results demonstrate that multi-modality feature aggregation is effective for optic neuropathy diseases diagnosis, and GFN achieved a five-classes classification accuracy of 87.82% on the test dataset. © 2020