Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans

Geographic atrophy (GA) is the defining lesion of advanced atrophic age-related macular degeneration (AMD). GA can be detected and characterized most accurately using spectral-domain optical coherence tomography (SDOCT), which provides detailed 3D information about changes in multiple retinal layers. Existing methods are limited to 2D convolutional neural networks (CNNs). Therefore, they do not capture the 3D context between adjacent 2D slices of the OCT scan and also require a large inference time. We propose 3D CNNs with 3D attention mechanisms for the automated detection of GA on SDOCT scans using scan-level labels. The best network achieved an accuracy of 88%, and its visualizations suggest the interpretability of its predictions.