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Sep 9, 2024 · In this study, a comparative analysis to determine the most suitable self-supervised learning methods for developing medical artificial intelligence models ...
In this study, we evaluated the efficacy of traditional supervised learning methods and four self-supervised learning methods on two publicly available OCT ...
Apr 15, 2024 · Our results suggest the SSL models provide superior performance under both the balanced and imbalanced training scenarios. The SSL model with ...
Mar 1, 2024 · The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled ...
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Apr 26, 2023 · One pioneering contrastive-based method is SimCLR, which outperformed supervised models on ImageNet benchmark using 100 times fewer labels.
Our proposed method achieves sensitivity, specificity, and accuracy of 89.74%, 94.87%, and 93.18%, respectively, on the clinical dataset. On the public dataset, ...
In this article, for a single OCT image, we propose a self-supervised deep learning model called Self2Self-OCT network by improved the Self2Self network and ...
Missing: Comparison classification.
Our study demonstrates the feasibility of learning valuable features for AMD classification without annotations, using a cross-modality contrastive learning ...
Nov 15, 2023 · We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, ...
Jan 22, 2024 · Our research contributes a self-supervised robust machine learning framework, OCT-SelfNet, for detecting eye diseases using optical coherence tomography (OCT) ...