Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915103623872512 |
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| author | Jian, Bailiang Pan, Jiazhen Ghahremani, Morteza Rueckert, Daniel Wachinger, Christian Wiestler, Benedikt |
| author_facet | Jian, Bailiang Pan, Jiazhen Ghahremani, Morteza Rueckert, Daniel Wachinger, Christian Wiestler, Benedikt |
| contents | Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5\% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with "more advanced" computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across diverse organs and modalities. The code is available at \url{https://github.com/BailiangJ/rethink-reg}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19274 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration Jian, Bailiang Pan, Jiazhen Ghahremani, Morteza Rueckert, Daniel Wachinger, Christian Wiestler, Benedikt Computer Vision and Pattern Recognition Image and Video Processing Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5\% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with "more advanced" computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across diverse organs and modalities. The code is available at \url{https://github.com/BailiangJ/rethink-reg}. |
| title | Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2407.19274 |