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Fast Predictive Image Registration
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“At first, Yang et al. (73) proposed to register brain MRI images using a U-Net-like structure in one step.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [73]
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An Unsupervised Single-Modal Medical Image Registration Algorithm Based on Deep Learning
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Metadata
- Authors
- Yang, X., Kwitt, R., & Niethammer, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Salzburg (AT)
- Corresponding author
- Yang, Xiao
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 48-57
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2482922118
- Cited by
- 127
- References
- 20
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 17 · 2022: 14 · 2021: 18 · 2020: 14 · 2019: 23 · 2018: 8 · 2017: 17
- FWCI
- 5.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- image registration, deformation prediction, LDDMM, diffeomorphic registration, Bayesian neural networks, uncertainty estimation, dropout, brain MRI registration, OASIS dataset
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