Semi-supervised learning with cross-localisation in shared GAN latent space for enhanced OCT data augmentation

Abstract

Deep learning methods have demonstrated stateof-the-art performance for the segmentation of the retina and choroid in optical coherence tomography (OCT) images. These methods are automatic and fast, yielding high accuracy and precision, thus reducing the load of manual analysis. However, deep learning usually requires large amounts of diverse, labelled data for training which can be difficult or infeasible to obtain, especially for medical images. For example, privacy concerns and lack of confidentiality agreements are common and are an obstacle to the sharing of useful training data. Additionally, some data can be significantly more difficult to obtain in the first place such as that of rare pathologies. Even in cases where sufficient data is available, the cost and time to perform image labelling can be significant. In many cases, data augmentation is employed to enhance the size of the training set. Similarly, semisupervised learning (SSL) can be used to exploit potentially large amounts of unlabeled data which would otherwise be unused. Motivated by this, in this study, we propose an enhanced StyleGAN2-based data augmentation method for OCT images by employing SSL through a novel crosslocalisation technique. For OCT image patches, the proposed method significantly improved the classification accuracy over the previous GAN data augmentation approach which uses labelled data only. The technique works by automatically learning, mixing, and injecting unlabelled styles into the labelled data to further increase the diversity of the synthetic data. The proposed method can be trained using differing quantities of both labelled and unlabelled data simultaneously. The method is simple, effective, generalizable and can be easily applied and used to extend StyleGAN2. Hence, there is also significant potential for the proposed method to be applied to other domains and imaging modalities for data augmentation purposes where unlabelled data exists.