3DUnetCNN | Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation | Machine Learning library

 by   ellisdg Python Version: v0.4 License: MIT

kandi X-RAY | 3DUnetCNN Summary

kandi X-RAY | 3DUnetCNN Summary

3DUnetCNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. 3DUnetCNN has no vulnerabilities, it has a Permissive License and it has medium support. However 3DUnetCNN has 5 bugs and it build file is not available. You can download it from GitHub.

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

            kandi-support Support

              3DUnetCNN has a medium active ecosystem.
              It has 1630 star(s) with 630 fork(s). There are 57 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 262 have been closed. On average issues are closed in 149 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of 3DUnetCNN is v0.4

            kandi-Quality Quality

              3DUnetCNN has 5 bugs (1 blocker, 0 critical, 3 major, 1 minor) and 95 code smells.

            kandi-Security Security

              3DUnetCNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              3DUnetCNN code analysis shows 0 unresolved vulnerabilities.
              There are 3 security hotspots that need review.

            kandi-License License

              3DUnetCNN is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              3DUnetCNN releases are not available. You will need to build from source code and install.
              3DUnetCNN has no build file. You will be need to create the build yourself to build the component from source.
              3DUnetCNN saves you 3859 person hours of effort in developing the same functionality from scratch.
              It has 8223 lines of code, 720 functions and 102 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed 3DUnetCNN and discovered the below as its top functions. This is intended to give you an instant insight into 3DUnetCNN implemented functionality, and help decide if they suit your requirements.
            • Run inference
            • Generate filenames from multiple datasets
            • Generate filenames from subject identifiers
            • Generate filenames
            • Make predictions from a config file
            • Fetch a model by name
            • Build or load a trained model
            • Train Keras training
            • Build a model
            • Write training data
            • Compute predictions with permutation keys
            • Visualize the Gaussian distribution
            • Calculates Volumetric predictions
            • Zero - one window
            • Train a model
            • Parse the command line options
            • Compute the Fourier Transform
            • Compute predictions for a given subject
            • Predict super resolution
            • Augments autoimplementation of a case
            • Generate an Isensee19 model
            • Get training and validation data
            • Perform a single - volume Zstat
            • Constructs a unet - trained Convolutional model
            • Crop the image
            • Estimate the Gaussian distribution
            Get all kandi verified functions for this library.

            3DUnetCNN Key Features

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

            3DUnetCNN Examples and Code Snippets

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

            Community Discussions


            Model fits on a single GPU but script crashes when trying to fit on multiple GPUs
            Asked 2018-Oct-19 at 13:58

            I have a model that can train fine on a single GPU, but when I try to fit it using multi_gpu_model, I get this CUDA error before the script exits:



            Answered 2018-Oct-19 at 13:58

            It turned out that the .fit() method of a multi_model_gpu did not like it when the number of samples in the dataset was not a multiple of the batch_size, i.e., the number of GPUs in my case. Ditching a sample from my dataset solved my issue. I reported this bug here.

            Source https://stackoverflow.com/questions/52724022

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


            No vulnerabilities reported

            Install 3DUnetCNN

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


            See FAQ, raise an issue on GitHub, or email me at davidgellis2@gmail.com.
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            gh repo clone ellisdg/3DUnetCNN

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