Strategies for Robust Deep Learning Based Deformable Registration
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909870848999424 |
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| author | Honkamaa, Joel Marttinen, Pekka |
| author_facet | Honkamaa, Joel Marttinen, Pekka |
| contents | Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, significantly hindering their usability. LUMIR brain registration challenge for Learn2Reg 2025 aims to advance the field by evaluating the performance of the registration on contrasts and modalities different from those included in the training set. Here we describe our submission to the challenge, which proposes a very simple idea for significantly improving robustness by transforming the images into MIND feature space before feeding them into the model. In addition, a special ensembling strategy is proposed that shows a small but consistent improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Strategies for Robust Deep Learning Based Deformable Registration Honkamaa, Joel Marttinen, Pekka Computer Vision and Pattern Recognition Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, significantly hindering their usability. LUMIR brain registration challenge for Learn2Reg 2025 aims to advance the field by evaluating the performance of the registration on contrasts and modalities different from those included in the training set. Here we describe our submission to the challenge, which proposes a very simple idea for significantly improving robustness by transforming the images into MIND feature space before feeding them into the model. In addition, a special ensembling strategy is proposed that shows a small but consistent improvement. |
| title | Strategies for Robust Deep Learning Based Deformable Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.23079 |