Publications¶

Research implemented in MIR¶

Paper	Venue	Links
Pretraining deformable image registration networks with random images	Chen, Junyu, et al. MIDL Short Papers, 2025	Paper \| Code
Correlation ratio for unsupervised learning of multi-modal deformable registration	Chen, Xiaojian, et al. SPIE Medical Imaging, 2025	Paper \| Code
Unsupervised learning of spatially varying regularization for diffeomorphic image registration	Chen, Junyu, et al. Medical Image Analysis, 2025	Paper \| Code
Vector field attention for deformable image registration	Liu, Yihao, et al. Journal of Medical Imaging, 2024	Paper \| Code
Unsupervised learning of multi-modal affine registration for PET/CT	Chen, Junyu, et al. IEEE NSS/MIC/RTSD, 2024	Paper \| Code
On finite difference Jacobian computation in deformable image registration	Liu, Yihao, et al. International Journal of Computer Vision, 2024	Paper \| Code
Deformable cross-attention transformer for medical image registration	Chen, Junyu, et al. MLMI Workshop, MICCAI 2023	Paper \| Code
Unsupervised learning of diffeomorphic image registration via TransMorph	Chen, Junyu, et al. WBIR, 2022	Paper \| Code
TransMorph: Transformer for unsupervised medical image registration	Chen, Junyu, et al. Medical Image Analysis, 2022	Paper \| Code

Method	Reference	Links
ConvexAdam: dual-optimization-based 3D registration	Siebert, Hanna, et al. IEEE TMI, 2024	Paper \| Code
SITReg: symmetric, inverse consistent and topology preserving registration	Honkamaa, Joel, and Pekka Marttinen. MELBA, 2024	Paper \| Code
MultiMorph: on-demand atlas construction	Abulnaga, S. Mazdak, et al. CVPR, 2025	Paper \| Code
Intensity normalization toolkit	Reinhold, Jacob C., et al. SPIE Medical Imaging, 2019	Paper \| Code
LessNet, Fourier-Net and Fourier-Net+	Jia, Xi, et al. MLMI 2022, AAAI 2023	LessNet \| Fourier-Net
FireANTs: GPU greedy and SyN registration	Jena, Rohit, et al.	Code
deedsBCV: MRF-based discrete registration	Heinrich, Mattias P., et al. IEEE TMI, 2013	Code

Please cite the paper of the model you use, as listed above and on each model page, and link to the MIR repository.