Issue 5
Nov 2021
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SHI Zhenwei, LIU Zaiyi. Attaching Importance to the Standardized Construction of Artificial Intelligence Database of Medical Imaging[J]. JOURNAL OF MECHANICAL ENGINEERING, 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]. JOURNAL OF MECHANICAL ENGINEERING, 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: 29 Jun 2021
  • Accepted Date: 29 Jul 2021
  • Available Online: 26 Nov 2021
  • Publish Date: 19 Aug 2021
  • Issue Publish Date: 30 Sep 2021
  • 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.

     

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  • [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
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