Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data

To meet the demands imposed by the continuing growth of the Age‐related macular degeneration (AMD) patient population, automation of follow‐ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing and labelling data for training is time consuming. In this study, we investigate the feasibility of training a convolutional neural network (CNN) to accurately detect retinal oedema on optical coherence tomography (OCT) images of AMD patients with labels derived directly from clinical treatment decisions, without extensive preprocessing or relabelling...