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30 million Optical Coherence Tomography (OCT) imaging tests are issuedannually to diagnose various retinal diseases, but accurate diagnosis of OCTscans requires trained eye care professionals who are still prone to makingerrors. With better systems for diagnosis, many cases of vision loss caused byretinal disease could be entirely avoided. In this work, we present ReLaX, anovel deep learning framework for explainable, accurate classification ofretinal pathologies which achieves state-of-the-art accuracy. Furthermore, weemphasize producing both qualitative and quantitative explanations of themodel's decisions. While previous works use pixel-level attribution methods forgenerating model explanations, our work uses a novel retinal layer attributionmethod for producing rich qualitative and quantitative model explanations.ReLaX determines the importance of each retinal layer by combining heatmapswith an OCT segmentation model. Our work is the first to produce detailedquantitative explanations of a model's predictions in this way. The combinationof accuracy and interpretability can be clinically applied for accessible,high-quality patient care.

ReLaX: Retinal Layer Attribution for Guided Explanations of Automated Optical Coherence Tomography Classification