1. A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.
- Author
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Jiachu D, Luo L, Xie M, Xie X, Guo J, Ye H, Cai K, Zhou L, Song G, Jiang F, Huang D, Zhang M, and Zheng C
- Subjects
- Humans, Cross-Sectional Studies, Multimodal Imaging methods, ROC Curve, Fluorescein Angiography methods, Fundus Oculi, Area Under Curve, Retinal Vein Occlusion diagnostic imaging, Retinal Vein Occlusion diagnosis, Deep Learning, Algorithms
- Abstract
Purpose: To propose and validate a meta-learning approach for detecting retinal vein occlusion (RVO) from multimodal images with only a few samples., Methods: In this cross-sectional study, we formulate the problem as meta-learning. The meta-training dataset consists of 1254 color fundus (CF) images from 39 different fundus diseases. Two meta-testing datasets include a public domain dataset and an independent dataset from Kandze Prefecture People's Hospital. The proposed meta-learning models comprise two modules: the feature extraction networks and the prototypical networks (PNs). We use two deep learning models (the ResNet and the Contrastive Language-Image Pre-Training networks [CLIP]) for feature extraction. We evaluate the performance of the algorithms using accuracy, area under the receiver operating characteristic curve (AUCROC), F1-score, and recall., Results: CLIP-based PNs outperform across all meta-testing datasets. For the public APTOS dataset, meta-learning algorithms achieve good results with an accuracy of 86.06% and an AUCROC of 0.87 with only 16 training images. In the hospital datasets, meta-learning algorithms show excellent diagnostic capability for detecting RVO with a very low number of shots (AUCROC above 0.99 for n = 4, 8, and 16, respectively). Notably, even though the meta-training dataset does not include fluorescein angiography (FA) images, meta-learning algorithms also have excellent diagnostic capability for detecting RVO from images with a different modality (AUCROC above 0.93 for n = 4, 8, and 16, respectively)., Conclusions: The proposed meta-learning models excel in detecting RVO, not only on CF images but also on FA images from a different imaging modality., Translational Relevance: The proposed meta-learning models could be useful in automatically detecting RVO on CF and FA images.
- Published
- 2024
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