Deep Learning in Ophthalmology
Recent works suggest novel deep learning tools for detection, segmentation and characterization of eye disorders. Accurate segmentation of retinal fundus lesions and anomalies in imaging data is an important technical step for early detection and treatment of common eye disorders, and a central algorithmic challenge for supervised learning approaches in this context if the sparsity of labeled data. We review several new works that suggest new tools and network architectures as deep learning in ophthalmology models to deal with this problem. [4] suggest a semi-supervised learning approach for segmentation of the