1. MULTI-MODAL LEARNING USING PHYSICIANS DIAGNOSTICS FOR OPTICAL COHERENCE TOMOGRAPHY CLASSIFICATION

    MULTI-MODAL LEARNING USING PHYSICIANS DIAGNOSTICS FOR OPTICAL COHERENCE TOMOGRAPHY CLASSIFICATION

    In this paper, we propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography (OCT) using multi-modal learning. To demonstrate the effectiveness of this approach, we create a medical diagnostic attribute dataset to improve disease classification using OCT. Although there have been successful at- tempts to deploy machine learning for disease classification in OCT, such methodologies lack the experts insights. We argue that injecting ophthalmological assessments as another supervision in a learning framework is of great importance for the machine learning process to perform accurate and interpretable classification. We demonstrate the proposed frame- work ...

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