Volume 12 Issue 5
Sep.  2021
Turn off MathJax
Article Contents
SHI Zhenwei, LIU Zaiyi. Attaching Importance to the Standardized Construction of Artificial Intelligence Database of Medical Imaging[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 599-601. doi: 10.12290/xhyxzz.2021-0507
Citation: SHI Zhenwei, LIU Zaiyi. Attaching Importance to the Standardized Construction of Artificial Intelligence Database of Medical Imaging[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 599-601. doi: 10.12290/xhyxzz.2021-0507

Attaching Importance to the Standardized Construction of Artificial Intelligence Database of Medical Imaging

doi: 10.12290/xhyxzz.2021-0507
Funds:

National Natural Science Foundation of China 81771912

National Natural Science Foundation of China 82102034

National Science Fund for Distinguished Young Scholars 81925023

More Information
  • Corresponding author: LIU Zaiyi  Tel: 86-20-83870125, E-mail: liuzaiyi@gdph.org.cn
  • Received Date: 2021-06-29
  • Accepted Date: 2021-07-29
  • Available Online: 2021-08-19
  • Publish Date: 2021-09-30
  • Medical imaging is regarded as one of the most potential domains where artificial intelligence can be applied in practice. However, artificial intelligence is facing challenges resulting from continuous growth of data, such as lack of high-quality data, lack of standardization in domain, lack of effective data management and regulation. Therefore, it is necessary to construct a standardized medical imaging database complying with the national condition of China, laws/regulations, and using habits of researchers. FAIR data principle (findable, accessible, interoperable, and reusable) may play a key role in database construction, data acquisition, and regulating descriptions of medical imaging data. Looking forward to boosting the standardized construction of artificial intelligence databases of medical imaging under the combined efforts of national researchers.
  • loading
  • [1] 互联网医疗健康产业联盟. 2018年医疗人工智能技术与应用白皮书[EB/OL ]. (2018-04-16)[2021-07-30]. http://www.qianjia.com/html/2018-04/16_289594.html.
    [2] Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18: 500-510. doi:  10.1038/s41568-018-0016-5
    [3] Bi WL, Hosny A, Schabath, MB, et al. Artificial intelli-gence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69: 127-157. http://www.onacademic.com/detail/journal_1000041692131499_13d7.html
    [4] Duncan JS, Insana MF, Ayache N. Biomedical imaging and analysis in the age of big data and deep learning[J]. Proc IEEE, 2019, 108: 3-10. http://ieeexplore.ieee.org/document/8944337/
    [5] Hartel FW, Coronado S, Dionne R, et al. Modeling a description logic vocabulary for cancer research[J]. J Biomed Inform, 2005, 38: 114-129. doi:  10.1016/j.jbi.2004.09.001
    [6] Zhou SK, Greenspan H, Davatzikos C, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises[J]. Proc IEEE, 2021, 109: 820-838. doi:  10.1109/JPROC.2021.3054390
    [7] Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository[J]. J Digit Imaging, 2013, 26: 1045-1057. doi:  10.1007/s10278-013-9622-7
    [8] Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge[J]. Contemp Oncol (Pozn), 2015, 19: A68. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.809.8713&rep=rep1&type=pdf
    [9] Vesteghem C, Brøndum RF, Sønderkær M, et al. Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives[J]. Brief Bioinform, 2020, 21: 936-945. doi:  10.1093/bib/bbz044
    [10] Wilkinson MD, Dumontier M, Sansone SA, et al. Evaluat-ing FAIR maturity through a scalable, automated, community-governed framework[J]. Sci Data, 2019, 6: 174. doi:  10.1038/s41597-019-0184-5
    [11] Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship[J]. Sci Data, 2016, 3: 160018. doi:  10.1038/sdata.2016.18
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (839) PDF downloads(497) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return