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

Concepedia

Publication | Open Access

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

DOI Full Paper Access

87

Citations

36

References

2020

Year

Olle Holmberg, Niklas Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel, Ben Asani, Johannes Schiefelbein, Siegfried Priglinger, Karsten Kortuem,

Nature Machine Intelligence

Diabetic RetinopathyEngineeringMachine LearningOphthalmologyData ScienceDeep LearningRetinaDiabetesSelf-supervised LearningFeature LearningVisual DiagnosisUnlabelled DataMedical Image ComputingMedicineComputer Vision

References

	Year	Citations

Page 1

Page 1