Brain-Tumor-Segmentation | Guided Version of 2D UNet | Machine Learning library
kandi X-RAY | Brain-Tumor-Segmentation Summary
kandi X-RAY | Brain-Tumor-Segmentation Summary
Brain-Tumor-Segmentation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. Brain-Tumor-Segmentation has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
The source code for our paper "Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation". Our paper can be found at this link.
The source code for our paper "Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation". Our paper can be found at this link.
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Brain-Tumor-Segmentation has a low active ecosystem.
It has 139 star(s) with 29 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 11 open issues and 6 have been closed. On average issues are closed in 47 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Brain-Tumor-Segmentation is current.
Quality
Brain-Tumor-Segmentation has 0 bugs and 0 code smells.
Security
Brain-Tumor-Segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Brain-Tumor-Segmentation code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Brain-Tumor-Segmentation 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.
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Brain-Tumor-Segmentation 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 are not available. Examples and code snippets are available.
Brain-Tumor-Segmentation saves you 239 person hours of effort in developing the same functionality from scratch.
It has 582 lines of code, 30 functions and 7 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Brain-Tumor-Segmentation and discovered the below as its top functions. This is intended to give you an instant insight into Brain-Tumor-Segmentation implemented functionality, and help decide if they suit your requirements.
- Train a model
- Construct a unetnet model
- Solve convolutional layer
- Resample a block of data
- Resh block
- Multi - layer block block
- 2D convolutional layer
- Custom loss function
- Gives the generalized Dice
- Gives the generalized Dice loss
- Create a table
- Read a brain
- Normalize volume
- Normalize a slice
- Save prediction results to output directory
Get all kandi verified functions for this library.
Brain-Tumor-Segmentation Key Features
No Key Features are available at this moment for Brain-Tumor-Segmentation.
Brain-Tumor-Segmentation Examples and Code Snippets
No Code Snippets are available at this moment for Brain-Tumor-Segmentation.
Community Discussions
Trending Discussions on Brain-Tumor-Segmentation
QUESTION
TypeError: Only integers, slices (`:`), ellipsis (`…`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got [1, 3]
Asked 2020-Sep-01 at 12:45
I am trying to train a 3D segmentation Network from Github. My model is implemented by Keras (Python) which is a typical U-Net model. The model, summary is given below,
...ANSWER
Answered 2020-Sep-01 at 11:23The error says it directly: you give [1,3] which is a list, where it expects either a number or a slice.
Maybe you meant [1:3] ?
You seem to give the [1,3] there so maybe should change:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Brain-Tumor-Segmentation
You can download it from GitHub.
You can use Brain-Tumor-Segmentation 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.
You can use Brain-Tumor-Segmentation 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 .
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