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