BioinformaticsNature Machine IntelligencePeer-reviewed
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…·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.