Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy
Olle Holmberg, Niklas Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel, Ben Asani, Johannes Schiefelbein, Siegfried Priglinger, Karsten Kortuem, Fabian J. Theis
Topics & Concepts
Optical coherence tomographyComputer scienceArtificial intelligenceDeep learningDiabetic retinopathyFundus (uterus)Pattern recognition (psychology)Medical imagingModalMachine learningSupervised learningRetinalMedicineArtificial neural networkOphthalmologyPolymer chemistryEndocrinologyDiabetes mellitusChemistryRetinal Imaging and AnalysisRetinal Diseases and TreatmentsDigital Imaging for Blood Diseases