Detecting Retinopathy from Optical Coherence Tomography Images Using a Novel Augmentation-based Semi-supervised Learning Approach

Optical coherence tomography (OCT) facilitates the diagnosis of retinal diseases with a non-invasive and rapid imaging solution. When automatically detecting retinopathy in OCT images using machine learning, Semi-Supervised Learning (SSL) technologies can reduce the burden on data annotation which is expensive and tedious. The conventional pseudo-label-based SSL often suffers from an accuracy decline due to confirmation bias. To address this, we propose an augmentation-based SSL method for the automatic detection of retinopathy in OCT images. Specifically, we employ both weak and strong augmentations on unlabeled images and learn a pre-trained deep network. Pseudo labels are derived from predictions on weakly-augmented OCT images, which diminishes confirmation bias and enhances the model’s robustness. We validate our method on two public OCT image datasets with 2% labels and 0.1% labels respectively, precisely detecting four types of retinal diseases. The experimental results show that we surpass existing state-of-the-art models in the retinopathy classification.