Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration | paper Learning a Model-Driven Variational Network

by xi-jia Python Version: Current License: MIT

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kandi X-RAY | Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration Summary

Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration is a Python library. Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration build file is not available. You can download it from GitHub.

This is the code for the paper Learning a Model-Driven Variational Network for Deformable Image Registration. If you find the code is somehow useful, please consider cite this paper. The current version is kind of messy and only contains the architectures. If you have any problems or notice any bugs related to this version, please fell free to contact me (x.jia.1@cs.bham.ac.uk). ~~We will update the code (such as including the training and testing code) in the future (hopefully soon).~~ The training and testing code can be found in our LKU-Net and Fourier-Net. They are bascially the same.

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Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration has no issues reported. There are no pull requests.

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Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration is licensed under the MIT License. This license is Permissive.

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You can use Learning-a-Model-Driven-Variational-Network-for-Deformable-Image-Registration like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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