Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping

Abstract

Glaucoma is an eye disease that occurs without the onset of symptoms at initial, and late diagnosis results in irreversible degeneration of retinal ganglion cells. Standard automated perimetry is the gold standard for assessing glaucoma; however, the examination is subjective, where responses can fluctuate each time the test is performed, significantly confounding the test’s interpretation. In this study, we present our approach that aims to provide a rapid point-of-care diagnostics for glaucoma patients by eliminating the cognitive aspect in existing visual field assessment. Unlike existing methods that mostly report the foveal target detection’s accuracy, we employed a multi-task learning architecture that efficiently captures signals simultaneously from the fovea and the neighboring targets in the peripheral vision, generating a visual response map. Furthermore, we designed a multi-task learning module that learns multiple tasks in parallel efficiently. We evaluated our model classification on a 40-classes dataset, with yields 92% and 95% in accuracy and F1 score respectively. Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics. Our proposed approach could be a stepping stone for an objective assessment of glaucoma patients’ visual field.