Self-supervised patient-specific features learning for OCT image classification

Deep learning's great success in image classification is heavily reliant on large-scale annotated datasets. However, obtaining labels for optical coherence tomography (OCT) data requires the significant effort of professional ophthalmologists, which hinders the application of deep learning in OCT image classification. In this paper, we propose a self-supervised patient-specific features learning (SSPSF) method to reduce the amount of data required for well OCT image classification results. Specifically, the SSPSF consists of a self-supervised learning phase and a downstream OCT image classification learning phase. The self-supervised learning phase contains two self-supervised patient-specific features learning tasks. One is to learn to discriminate an OCT scan which belongs to a specific patient. The other task is to learn the invariant features related to patients. In addition, our proposed self-supervised learning model can learn inherent representations from the OCT images without any manual labels, which provides well initialization parameters for the downstream OCT image classification model. The proposed SSPSF achieves classification accuracy of 97.74% and 98.94% on the public RETOUCH dataset and AI Challenger dataset, respectively. The experimental results on two public OCT datasets show the effectiveness of the proposed method compared with other well-known OCT image classification methods with less annotated data.