Korean J Radiol.
2021 Dec;22(12):2073-2081. 10.3348/kjr.2021.0170.
An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research
- Affiliations
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- 1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 2Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
- 3Department of Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 4Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 5Bigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Korea
- 6Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- KMID: 2522944
- DOI: http://doi.org/10.3348/kjr.2021.0170
Abstract
- 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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