Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy

Olle G. Holmberg, Niklas D. Köhler, Thiago Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel, Ben Asani, Johannes Schiefelbein, Siegfried Priglinger, Karsten U. Kortuem, Fabian J. Theis

Abstract: The thickness of the retina is an important medical indicator for diabetic retinopathy. Holmberg and colleagues present a self-supervised deep-learning method that uses cross-modal data to predict retinal thickness maps from easily obtainable fundus images.