2021/09/03 by Evan Wen, Wen, Evan, Rebecca Sorenson +3
Computer Science · Medicine · Psychology · #Artificial intelligence #Attribution #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Interpretability #Machine Learning (cs.LG) #Machine Learning in Healthcare #Machine learning #Medicine #Ophthalmology #Optical coherence tomography #Optometry #Pattern recognition (psychology) #Psychology #Retinal #Retinal Imaging and Analysis #Segmentation #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.02436
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.