torchio | Medical imaging toolkit for deep learning | Machine Learning library

 by   fepegar Python Version: 0.19.3 License: Apache-2.0

kandi X-RAY | torchio Summary

kandi X-RAY | torchio Summary

torchio is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. torchio has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However torchio build file is not available. You can install using 'pip install torchio' or download it from GitHub, PyPI.

Tools like TorchIO are a symptom of the maturation of medical AI research using deep learning techniques. Jack Clark, Policy Director at OpenAI (link). (Queue for patch-based training). TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts. This package has been greatly inspired by NiftyNet, which is not actively maintained anymore.

            kandi-support Support

              torchio has a medium active ecosystem.
              It has 1743 star(s) with 207 fork(s). There are 17 watchers for this library.
              There were 5 major release(s) in the last 6 months.
              There are 36 open issues and 401 have been closed. On average issues are closed in 70 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of torchio is 0.19.3

            kandi-Quality Quality

              torchio has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              torchio is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              torchio releases are available to install and integrate.
              Deployable package is available in PyPI.
              torchio has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 8709 lines of code, 759 functions and 131 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed torchio and discovered the below as its top functions. This is intended to give you an instant insight into torchio implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Load image data
            • Calculate the average map for the given database
            • Return a numpy array of percentile values
            • Compute the center of the mask
            • Compute cropping and crop params
            • Calculate the bounding box of a volume
            • Computes the crop and padding parameters
            • Apply the transformation to a subject
            • Apply transform to a subject
            • Return list of subjects
            • Apply transformation to the image
            • Applies the transform to a set of images
            • Apply a transformation to a subject
            • Show the image
            • Add images from a batch of subjects
            • Apply transforms to a subject
            • Get a list of subjects
            • Get a dictionary of transformers
            • Calculate the inverse transform
            • Apply transforms to the image
            • Apply transformation to a subject
            • Download and extract the archive
            • Applies transforms to a subject
            • Plot a gif image
            • Apply blur to images
            Get all kandi verified functions for this library.

            torchio Key Features

            No Key Features are available at this moment for torchio.

            torchio Examples and Code Snippets

            Hippocampus Segmentation from MRI using V-Net,Usage,Validation
            Pythondot img1Lines of Code : 2dot img1License : Permissive (MIT)
            copy iconCopy
            python run/ --dir=path/to/logs/dir --write=WRITE --verbose=VERBOSE
            python run/ --dir=path/to/logs/dir --verbose=VERBOSE
            Hippocampus Segmentation from MRI using V-Net,Usage,Training
            Pythondot img2Lines of Code : 1dot img2License : Permissive (MIT)
            copy iconCopy
            python run/ --epochs=NUM_EPOCHS --batch=BATCH_SIZE --workers=NUM_WORKERS --lr=LR

            Community Discussions


            How to pass dictionary elements from hydra config file
            Asked 2021-Nov-22 at 12:51

            I am trying to instantiate objects with hydra, I have a class torchio.transforms.RemapLabels that I am using in my config file:



            Answered 2021-Nov-09 at 04:09

            There are two options: you can pass the inputs as positional arguments or as named arguments.

            Using named arguments (a.k.a. keyword arguments) in your yaml file:


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


            No vulnerabilities reported

            Install torchio

            See Getting started for installation instructions and a Hello, World! example. Longer usage examples can be found in the tutorials. All the documentation is hosted on Read the Docs. Please open a new issue if you think something is missing.


            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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            pip install torchio

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            gh repo clone fepegar/torchio

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