BraTS18-Project | Brain tumor segmentation using fully-convolutional deep | Machine Learning library

by jondeaton Python Version: Current License: No License

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kandi X-RAY | BraTS18-Project Summary

BraTS18-Project is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. BraTS18-Project has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Brain tumor segmentation using fully-convolutional deep neural networks.

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BraTS18-Project has a low active ecosystem.

It has 46 star(s) with 15 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 57 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of BraTS18-Project is current.

Quality

BraTS18-Project has 0 bugs and 0 code smells.

Security

BraTS18-Project has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

BraTS18-Project code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BraTS18-Project 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

BraTS18-Project releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

BraTS18-Project saves you 861 person hours of effort in developing the same functionality from scratch.

It has 1971 lines of code, 173 functions and 33 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed BraTS18-Project and discovered the below as its top functions. This is intended to give you an instant insight into BraTS18-Project implemented functionality, and help decide if they suit your requirements.

Train the model
Dice between two segments
Calculate the dice coefficient
Get a slice for a given segment
Parse command line arguments
Unetables the input shape
Create a convolutional block of data
A block - wise convolution layer
Generate the model
Adds dropout to input
Generate a random partitioning dataset
Normalize the Brats dataset
Create training data pipeline
Get background mask from input directory
Restore model from given file
Load training and validation datasets
Make TFRecord files for each patient
Return a generator of patient objects
Wrapper function for conversion
Calculate the index of the patches per image
Return the validation directory
Return the directory containing the training data set
Get the survival data for the survival data
Return the validation csv file
Returns the train survival csv file
Return a dictionary of training dir_map

Get all kandi verified functions for this library.

BraTS18-Project Key Features

No Key Features are available at this moment for BraTS18-Project.

BraTS18-Project Examples and Code Snippets

No Code Snippets are available at this moment for BraTS18-Project.

Community Discussions

Trending Discussions on BraTS18-Project

Restore TensorFlow model on different machine

QUESTION

Restore TensorFlow model on different machine

Asked 2018-Jun-23 at 02:51

I trained on TensorFlow model on a GPU cluster, saved the model using

...

ANSWER

Answered 2018-Jun-23 at 02:10

Open up the checkpoint file with your favorite text editor and simply change the absolute paths found therein to just filenames.

Source https://stackoverflow.com/questions/50751484

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

Vulnerabilities

No vulnerabilities reported

Install BraTS18-Project

Install the required dependencies.

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 .

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