Explainable Deep Learning Methods for Ophthalmic Diagnosis

W. Waterloo, ON, N2L 3G1, Canada Email [email protected] Background: The lack of explanations for the decisions made by deep learning algorithms has hampered their acceptance by the clinical community despite highly accurate results on multiple problems. Attribution methods explaining deep learning models have been tested on medical imaging problems. The performance of various attribution methods has been compared for models trained on standard machine learning datasets but not on medical images. In this study, we performed a comparative analysis to determine the method with the best explanations for retinal OCT diagnosis. Methods: A well-known deep learning model, Inception-v3 was trained to diagnose 3 retinal diseases – choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen. The explanations from 13 different attribution methods were rated by a panel of 14 clinicians for clinical significance.