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CITATION.cff
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cff-version: 1.2.0
title: fastMONAI
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Satheshkumar
family-names: Kaliyugarasan
email: [email protected]
affiliation: >-
Department of Computer Science, Electrical Engineering
and Mathematical Sciences, Western Norway University
of Applied Sciences, Bergen, Norway
orcid: 'https://orcid.org/0000-0002-0038-5540'
- given-names: Lundervold
family-names: Alexander Selvikvåg
email: [email protected]
affiliation: >-
Department of Computer Science, Electrical Engineering
and Mathematical Sciences, Western Norway University
of Applied Sciences, Bergen, Norway
orcid: 'https://orcid.org/0000-0001-8663-4247'
repository-code: 'https://github.com/MMIV-ML/fastMONAI'
url: 'https://fastmonai.no'
abstract: >-
A low-code Python-based open-source deep learning library
built on top of fastai, MONAI, and TorchIO.
fastMONAI simplifies using state-of-the-art deep learning
techniques in 3D medical image analysis for solving
classification, regression, and segmentation tasks.
fastMONAI provides users with functionalities to step
through data loading, preprocessing, training, and result
interpretations.
license: Apache-2.0
preferred-citation:
type: article
authors:
- family-names: " Kaliyugarasan"
given-names: "Satheshkumar"
orcid: "https://orcid.org/0000-0002-0038-5540"
- family-names: "Lundervold"
given-names: "Alexander Selvikvåg"
orcid: "https://orcid.org/0000-0001-8663-4247"
doi: "10.1016/j.simpa.2023.100583"
journal: "Software Impacts"
title: "fastMONAI: A low-code deep learning library for medical image analysis"
year: 2023