Evidence map›Paper›PMID 34719891›Full record

ReviewKorean journal of radiology2021

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

Sungchul Kim, Sungman Cho, Kyungjin Cho, Jiyeon Seo, Yujin Nam, Jooyoung Park, Kyuri Kim, Daeun Kim, Jeongeun Hwang, Jihye Yun and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sungchul Kim *Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-0258-1035
Sungman Cho *Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0002-7922-9638
Kyungjin ChoDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-8111-5058
Jiyeon SeoDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-0497-6855
Yujin NamDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-0786-5598
Jooyoung ParkDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-7641-3827
Kyuri KimAsan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0002-7707-1275
Daeun KimAsan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0001-7958-9564
Jeongeun HwangDepartment of Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5821-5405
Jihye YunDepartment of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5233-6687
Miso JangDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-4409-411X
Hyunna LeeBigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0000-0003-1669-3086
Namkug KimDepartment of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-3438-2217

Funding

Korea Health Industry Development Institute HI18C0022Korea Health Industry Development Institute HI18C2383
6 · The paper itself

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.

Indexed as

Deep LearningRadiologyDatabases, FactualHumansRadiologistsSoftwareDeep learningDownstream taskMedical imagingOpen platformPre-trained model

Identifiers

PMID34719891
PMCPMC8628158

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.