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An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping r...

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Detalles Bibliográficos
Autores principales: Kim, Sungchul, Cho, Sungman, Cho, Kyungjin, Seo, Jiyeon, Nam, Yujin, Park, Jooyoung, Kim, Kyuri, Kim, Daeun, Hwang, Jeongeun, Yun, Jihye, Jang, Miso, Lee, Hyunna, Kim, Namkug
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628158/
https://www.ncbi.nlm.nih.gov/pubmed/34719891
http://dx.doi.org/10.3348/kjr.2021.0170
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author Kim, Sungchul
Cho, Sungman
Cho, Kyungjin
Seo, Jiyeon
Nam, Yujin
Park, Jooyoung
Kim, Kyuri
Kim, Daeun
Hwang, Jeongeun
Yun, Jihye
Jang, Miso
Lee, Hyunna
Kim, Namkug
author_facet Kim, Sungchul
Cho, Sungman
Cho, Kyungjin
Seo, Jiyeon
Nam, Yujin
Park, Jooyoung
Kim, Kyuri
Kim, Daeun
Hwang, Jeongeun
Yun, Jihye
Jang, Miso
Lee, Hyunna
Kim, Namkug
author_sort Kim, Sungchul
collection PubMed
description Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.
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spelling pubmed-86281582021-12-07 An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research Kim, Sungchul Cho, Sungman Cho, Kyungjin Seo, Jiyeon Nam, Yujin Park, Jooyoung Kim, Kyuri Kim, Daeun Hwang, Jeongeun Yun, Jihye Jang, Miso Lee, Hyunna Kim, Namkug Korean J Radiol Technology, Experiment, and Physics Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet. The Korean Society of Radiology 2021-12 2021-10-26 /pmc/articles/PMC8628158/ /pubmed/34719891 http://dx.doi.org/10.3348/kjr.2021.0170 Text en Copyright © 2021 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technology, Experiment, and Physics
Kim, Sungchul
Cho, Sungman
Cho, Kyungjin
Seo, Jiyeon
Nam, Yujin
Park, Jooyoung
Kim, Kyuri
Kim, Daeun
Hwang, Jeongeun
Yun, Jihye
Jang, Miso
Lee, Hyunna
Kim, Namkug
An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title_full An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title_fullStr An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title_full_unstemmed An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title_short An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
title_sort open medical platform to share source code and various pre-trained weights for models to use in deep learning research
topic Technology, Experiment, and Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628158/
https://www.ncbi.nlm.nih.gov/pubmed/34719891
http://dx.doi.org/10.3348/kjr.2021.0170
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