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Mutualreg: Mutual Learning for Unsupervised Medical ...
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由 J Liu 著作2024 — Abstract: Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field.
Mutual Learning for Unsupervised Medical Image ...
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In this paper, we proposed multi-modal medical image registration method, which introduces mathematical morphology into image processing, and makes full use of ...
mutual learning for unsupervised medical image registration
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2024年4月11日 — MUTUALREG: MUTUAL LEARNING FOR UNSUPERVISED MEDICAL IMAGE REGISTRATION · 1: PATIENT-ADAPTIVE AND LEARNED MRI DATA UNDERSAMPLING USING ...
mutualreg: mutual learning for unsupervised medical image
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由 J Liu 著作2024 — We propose a novel mutual learning paradigm (MutualReg) for unsupervised medical image registration. Through Mutu-. alReg, knowledge is transferred mutually ...
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MutualReg/README.md at main
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Official Pytorch Code for the paper "MutualReg: Mutual Learning for Unsupervised Medical Image Registration" , presented at ICASSP 2024. The code is preparing..
Jun Liu - Google 学术搜索
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Mutualreg: Mutual Learning for Unsupervised Medical Image Registration. J Liu, W Wang, N Shen, W Wang, K Wang, Q Li, Y Yuan, H Zhang, G Luo. ICASSP 2024-2024 ...
(PDF) Research on Medical Image Registration Based ...
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2024年10月22日 — Medical image registration based on deep learning ... Conference Paper. Mutualreg: Mutual Learning for Unsupervised Medical Image Registration.
MDFlow: Unsupervised Optical Flow Learning by Reliable ...
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This work proposes a novel mutual distillation framework to transfer reliable knowledge back and forth between the teacher and student networks for ...
Mutual information neural estimation for unsupervised multi ...
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由 G Snaauw 著作2022被引用 8 次 — We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
Mutual information for unsupervised deep learning image ...
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由 BD de Vos 著作2020被引用 32 次 — Current unsupervised deep learning-based image registration methods are trained with mean squares or normalized cross correlation as a similarity metric.