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
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
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.
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
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