Domain and label efficient approach for diabetic retinopathy severity detection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16908-3.pdf
Reference87 articles.
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2. Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L et al (2020) Idrid: Diabetic retinopathy-segmentation and grading challenge. Med Image Anal 59:101561
3. Cavan D, Makaroff L, da Rocha Fernandes J, Sylvanowicz M, Ackland P, Conlon J, Chaney D, Malhi A, Barratt J (2017) The diabetic retinopathy barometer study: global perspectives on access to and experiences of diabetic retinopathy screening and treatment. Diabetes Res Clin Pract 129:16–24
4. Shurrab S, Duwairi R (2021) Self-supervised learning methods and applications in medical imaging analysis: A survey. arXiv:2109.08685
5. Yoo Y, Brosch T, Traboulsee A, Li DK, Tam R (2014) Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In: International Workshop on Machine Learning in Medical Imaging, pp. 117–124. Springer
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1. Self-supervised approach for diabetic retinopathy severity detection using vision transformer;Progress in Artificial Intelligence;2024-06-23
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