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Install

Install this package:

emerge -a sci-libs/torchio

If the package is masked, you can unmask it using the autounmask tool or standard emerge options:

autounmask sci-libs/torchio

Or alternatively:

emerge --autounmask-write -a sci-libs/torchio

Package Information

Description:

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.

Homepage:

https://torchio.readthedocs.io/

License:

Apache-2.0

Versions

Metadata

Description

• 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.

Maintainers

• Hendrik Klug (klugh@ethz.ch) - Type: person

Upstream

• Remote IDs:
  • github: fepegar/torchio
  • pypi: torchio

<pkgmetadata>
	<maintainer type="person">
		<email>klugh@ethz.ch</email>
		<name>Hendrik Klug</name>
	</maintainer>
	<longdescription>
        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.
    </longdescription>
	<upstream>
		<remote-id type="github">fepegar/torchio</remote-id>
		<remote-id type="pypi">torchio</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

• [Error] Missing md5-cache for ebuild torchio-0.17.42

Manifest