Self-Supervised Deep Learning—The Next Frontier
Author:
Affiliation:
1. Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
2. The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
3. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
Publisher
American Medical Association (AMA)
Link
https://jamanetwork.com/journals/jamaophthalmology/articlepdf/2814756/jamaophthalmology_liu_2024_ic_230063_1710970270.83327.pdf
Reference8 articles.
1. Self-supervised learning for improved optical coherence tomography detection of macular telangiectasia type 2.;Gholami;JAMA Ophthalmol
2. Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation.;Wang;Front Med (Lausanne),2022
3. Self-supervised feature learning and phenotyping for assessing age-related macular degeneration using retinal fundus images.;Yellapragada;Ophthalmol Retina,2022
4. Detection of stroke with retinal microvascular density and self-supervised learning using OCT-A and fundus imaging.;Pachade;J Clin Med,2022
5. Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy.;Holmberg;Nat Mach Intell,2020
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