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

• Published in: Korean journal of radiology Vol. 22; no. 12; pp. 2073 - 2081
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Korea (South) The Korean Society of Radiology 01-12-2021; 대한영상의학회
• Subjects: Algorithms; Artificial intelligence; Brain research; Classification; Databases, Factual; Datasets; Deep Learning; Humans; Liver; Morphology; Radiologists; Radiology; Software; Technology, Experiment, and Physics; 방사선과학; Deep learning; Open platform; Downstream task; Medical imaging; Pre-trained model
• ISSN: 1229-6929; 2005-8330
• DOI: 10.3348/kjr.2021.0170

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.