Authors:Yuli Wang, Hyewon Jung, Dongshen Peng, Yuwei Dai, Jing Wu, Haoyue Guan, Yoko Kato, Zhicheng Jiao, Yu Sun, Ihab Kamel, Joao Lima, Cheng Ting Lin, Harrison Bai
Abstract:Vision Transformer (ViT) models, utilizing self-attention mechanisms, have demonstrated robust generalization capabilities across various vision tasks, including image classification. However, these models, typically pretrained on general public datasets, often lack the specialized domain knowledge necessary for medical imaging applications. In this study, we investigate the adaptation of ViT models, specifically for cardiac magnetic resonance (MR) images, using an in-house dataset. We found that pretrained ViT features do not effectively transfer to the cardiac MR domain. To overcome this limitation, we introduce an adaptation strategy that utilizes image-based self-supervised contrastive learning, demonstrating superior performance compared to traditional supervised training approaches. Moreover, our adapted ViT model exhibits strong generalization to external MR datasets such as BraTS and ADNI. Through ablation studies, we further investigate the impact of batch size and dataset scale on performance. Ultimately, our adapted model achieves classification AUC exceeding 0.75 across the four most common cardiac MR sequences.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2605.24789 [cs.CV] |
| (or arXiv:2605.24789v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24789 arXiv-issued DOI via DataCite (pending registration) |
From: Yuli Wang [view email]
[v1]
Sun, 24 May 2026 00:24:50 UTC (2,144 KB)
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