Interpretation of deep learning using attributions: application to ophthalmic diagnosis

Amitojdeep Singh; Abdul Rasheed Mohammed; John Zelek; Vasudevan Lakshminarayanan

doi:10.1117/12.2568631

20 August 2020 Interpretation of deep learning using attributions: application to ophthalmic diagnosis

Amitojdeep Singh, Abdul Rasheed Mohammed, John Zelek, Vasudevan Lakshminarayanan

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110A (2020) https://doi.org/10.1117/12.2568631
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Optical coherence tomography (OCT) and retinal fundus images are widely used for detecting retinal pathology. In particular, these images are used by deep learning methods for classification of retinal disease. The main hurdle for widespread deployment of AI-based decision making in healthcare is a lack of interpretability of the cutting-edge deep learning-based methods. Conventionally, decision making by deep learning methods is considered to be a black box. Recently, there is a focus on developing techniques for explaining the decisions taken by deep neural networks, i.e. Explainable AI (XAI) to improve their acceptability for medical applications. In this study, a framework for interpreting the decision making of a deep learning network for retinal OCT image classification is proposed. An Inception-v3 based model was trained to detect choroidal neovascularization (CNV), diabetic macular edema (DME) and drusen from a dataset of over 80,000 OCT images. We visualized and compared various interpretability methods for the three disease classes. The attributions from various approaches are compared and discussed with respect to clinical significance. Results showed a successful attribution of the specific pathological regions of the OCT that are responsible for a given condition in the absence of any pixel-level annotations.

Conference Presentation

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Amitojdeep Singh, Abdul Rasheed Mohammed, John Zelek, and Vasudevan Lakshminarayanan "Interpretation of deep learning using attributions: application to ophthalmic diagnosis", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110A (20 August 2020); https://doi.org/10.1117/12.2568631

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