Interactive Deep Learning for Explainable Retinal Disease Image Classification

Age-related macular degeneration is the leading cause of vision deterioration among older adults. The selected treatment for this retinal disease is largely dependent on the later stage type (wet or geographic atrophy), meaning correct type classification is crucial to ensuring the best patient outcome. Previous studies have demonstrated high classification accuracy with medical images and used saliency maps to add explainability to opaque deep learning models. However, these explanations have revealed a tendency to make classification decisions based on irrelevant information. Our proposed deep learning model allows domain experts to correct model behavior during the training process through direct annotations of the regions of interest (ROIs) and integrates these annotations into the learning model. Our approach performs consistently with non-interactive classification accuracy of the retinal optical coherence tomography (OCT) scans. Filters are applied regionally to the original OCT image based on the annotations and Grad-CAM highlighted regions. Four interactive classification methods are introduced and compared against a non-interactive CNN with the same overall architecture. Three of the four methods selectively filter regions of the images with weighted pairs of enhancement and blurring filters. The fourth uses ROI maps to focus the attention of the feature maps on the expert annotated region(s). All overlap scores measuring the human and computer output agreement overperformed the non-interactive CNN baseline model with two of the interactive methods doubling the overlap score while another tripling the overlap score.