Enhancing Self-Supervised Learning for Rare Diseases in OCT-Reference-Cited by-同舟云学术

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Enhancing Self-Supervised Learning for Rare Diseases in OCT

Published:2024-07-01 Issue:7 Volume:142 Page:688
ISSN:2168-6165
Container-title:JAMA Ophthalmology
language:en
Short-container-title:JAMA Ophthalmol

Author:

Walston Shannon L.¹,Sato Shingo²,Ueda Daiju³⁴

Affiliation:

1. Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan

2. Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania

3. Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan

4. Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan

Publisher

American Medical Association (AMA)

Link

https://jamanetwork.com/journals/jamaophthalmology/articlepdf/2819805/jamaophthalmology_walston_2024_le_240008_1719935976.18347.pdf

Reference5 articles.

1. Self-supervised learning for improved optical coherence tomography detection of macular telangiectasia type 2.;Gholami;JAMA Ophthalmol,2024

2. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.;Khan;Lancet Digit Health,2021

3. Fairness of artificial intelligence in healthcare: review and recommendations.;Ueda;Jpn J Radiol,2024

4. Lowy Medical Research Institute. The MacTel Project. Accessed February 22, 2024. https://www.lmri.net/mactel/the-mactel-project/

5. Grill? JB, Strub? F, Altché? F, . Bootstrap your own latent: a new approach to self-supervised learning.? Adv Neural Inf Process Syst. 2020:21271-21284.

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