Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy
Olle Holmberg, Niklas Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Franziska Keidel, Ben Asani, Johannes Benedikt Schiefelbein, Siegfried Georg Priglinger, Karsten Kortuem, Fabian Joachim Theis
The paper proposes a self-supervised method to predict retinal thickness from fundus images, enabling efficient diabetic retinopathy classification with fewer annotations.
Nature Machine Intelligence · Nov 9, 2020Peer-reviewedBioinformatics