python-pillow/Pillow: 8.4.0

Kemenade, Hugo van; Murray, Andrew; wiredfool,; Clark, Alex M.; Karpinsky, Alexander; Baranovič, Ondrej; Gohlke, Christoph; Dufresne, Jon; Crowell, Brian; Schmidt, David A.; Kopachev, Konstantin; Houghton, Alastair; Mani, Sandro; Landey, Steve; vashek,; Ware, Josh; Douglas, Jason; Stanislau, T; Caro, David; Martinez, Uriel; Kossouho, Steve; Lahd, Riley; Lee, Antony; Brown, Eric W.; Tonnhofer, Oliver; Bonfill, Mickael; Rowlands, Peter; Al-Saidi, Fahad; Novikov, German

doi:10.5281/zenodo.5571504

Cited by 1 publication

(1 citation statement)

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“…They can be easily composed into optimized medical data processing pipelines emphasizing reproducible results. A non-exhaustive list of examples is provided as follows: LoadImage, which loads medical images with an automatically-selected package (ITK [13], Nibabel [14], PIL [15], etc. ); Spacing, which resamples input images into a specified voxel spacing; Orientation, which transforms images to a specified orientation axis (e.g., RAS); RandGaussianNoise, which perturbs image intensities by adding statistical noise; NormalizeIntensity, which normalizes data intensity based on mean and standard deviation; Affine, which transforms images based on affine parameters; and Rand3DElastic, which performs both a random elastic and affine deformations in 3D.…”

Section: Transformsmentioning

confidence: 99%

MONAI: An open-source framework for deep learning in healthcare

Cardoso¹,

Li²,

Brown³

et al. 2022

Preprint

101

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Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, welldocumented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

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Section: Transformsmentioning

confidence: 99%