3DUnet | Medical images segmentation with 3D UNet GAN | Machine Learning library

by HandsomeBrotherShuaiLi Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | 3DUnet Summary

3DUnet is a Python library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. 3DUnet has no bugs, it has no vulnerabilities and it has low support. However 3DUnet build file is not available. You can download it from GitHub.

Medical images segmentation with 3D UNet GAN

Support

Quality

Security

License

Reuse

Support

3DUnet has a low active ecosystem.

It has 17 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 97 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of 3DUnet is current.

Quality

3DUnet has no bugs reported.

Security

3DUnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

3DUnet does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

3DUnet releases are not available. You will need to build from source code and install.

3DUnet has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed 3DUnet and discovered the below as its top functions. This is intended to give you an instant insight into 3DUnet implemented functionality, and help decide if they suit your requirements.

Train the dataset
Preprocess a dynamic dataset
Extract patches from input volumes
Generate training data
Train the model
Train the train function
Builds the model
Compute the discriminator
1D convolutional W
Get a variable from the given ema
Generate examples
Find folder and patch ID
Split v2 folder into folder
Build the network
Build the model
Disconnects the network
Preprocess the dynamic unlab dataset
Extract patches from input images
Preprocess pre - processing
Normalise noise
Corrects a bias field correction
Predict the model
Process the image

Get all kandi verified functions for this library.

3DUnet Key Features

No Key Features are available at this moment for 3DUnet.

3DUnet Examples and Code Snippets

No Code Snippets are available at this moment for 3DUnet.

Community Discussions

Trending Discussions on 3DUnet

TensorFlow MirroredStrategy() not working for multi-gpu training

QUESTION

TensorFlow MirroredStrategy() not working for multi-gpu training

Asked 2020-Jan-30 at 20:08

I am trying to implement TensorFlows MirroredStrategy() to run a 3DUNet on 2 Nvidia Titan RTX graphics cards. The code is verified to work for 1 GPU. My OS is Red Hat Enterprise Linux 8 (RHEL8). The error comes at model.fit().

I have installed the appropriate NCCL Nvidia Drivers and verified that I can parse the training data onto both GPUs using an example from tensorflow.org.

Code:

...

ANSWER

Answered 2020-Jan-30 at 20:08

This answer is based on a comment on OP's question.

When conducting multi-gpu training with tf.distribute.MirroredStrategy, one should use the tf.keras API and not the tensorflow backend of the keras package.

In general, it is best not to mix tf.keras and keras.

Source https://stackoverflow.com/questions/59993176

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install 3DUnet

You can download it from GitHub.
You can use 3DUnet 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: