Pretraining Deformable Image Registration Networks with Random Images
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913866798071808 |
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| author | Chen, Junyu Wei, Shuwen Liu, Yihao Carass, Aaron Du, Yong |
| author_facet | Chen, Junyu Wei, Shuwen Liu, Yihao Carass, Aaron Du, Yong |
| contents | Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work showed that DNNs trained on randomly generated images with carefully designed noise and contrast properties can still generalize well to unseen medical data. Building on this insight, we propose using registration between random images as a proxy task for pretraining a foundation model for image registration. Empirical results show that our pretraining strategy improves registration accuracy, reduces the amount of domain-specific data needed to achieve competitive performance, and accelerates convergence during downstream training, thereby enhancing computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pretraining Deformable Image Registration Networks with Random Images Chen, Junyu Wei, Shuwen Liu, Yihao Carass, Aaron Du, Yong Computer Vision and Pattern Recognition Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work showed that DNNs trained on randomly generated images with carefully designed noise and contrast properties can still generalize well to unseen medical data. Building on this insight, we propose using registration between random images as a proxy task for pretraining a foundation model for image registration. Empirical results show that our pretraining strategy improves registration accuracy, reduces the amount of domain-specific data needed to achieve competitive performance, and accelerates convergence during downstream training, thereby enhancing computational efficiency. |
| title | Pretraining Deformable Image Registration Networks with Random Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.24167 |