Few-Shot Transfer Learning for Hereditary Retinal Diseases Recognition

This project aims to recognize a group of rare retinal diseases, the hereditary macular dystrophies, based on Optical Coherence Tomography (OCT) images, whose primary manifestation is the interruption, disruption, and loss of the layers of the retina. The challenge of using machine learning models to recognize those diseases arises from the limited number of collected images due to their rareness. We formulate the problems caused by lacking labeled data as a Student-Teacher learning task with a discriminative feature space and knowledge distillation (KD). OCT images have large variations due to different types of macular structural changes, capturing devices, and angles. To alleviate such issues, a pipeline of preprocessing is first utilized for image alignment. Tissue images at different angles can be roughly calibrated to a horizontal state for better feature representation. Extensive experiments on our dataset demonstrate the effectiveness of the proposed approach.