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Volume 14 Issue 1
Jan.  2023
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YANG Yiguang, WANG Juncheng, XIE Fengying, LIU Jie. Data and Methods in Computer-aided Diagnosis Systems of Skin Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 168-176. DOI: 10.12290/xhyxzz.2022-0413
Citation: YANG Yiguang, WANG Juncheng, XIE Fengying, LIU Jie. Data and Methods in Computer-aided Diagnosis Systems of Skin Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 168-176. DOI: 10.12290/xhyxzz.2022-0413
shu

Data and Methods in Computer-aided Diagnosis Systems of Skin Diseases

  • 1.

    Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China

  • 2.

    Department of Dermatology, National Clinical Research Center for Dermatologic and Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

  • 3.

    State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

Funds: 

National Natural Science Foundation of China 61871011

National Natural Science Foundation of China 62071011

National Natural Science Foundation of China 82173449

National Natural Science Foundation of China 61971443

National High Level Hospital Clinical Research Funding 2022-PUMCH-B-092

CAMS Innovation Fund for Medical Sciences 2022-I2M-C & T-A-007

More Information
  • Corresponding author:

    XIE Fengying, E-mail: xfy_73@buaa.edu.cn

    LIU Jie, E-mail: Liujie04672@pumch.cn

  • Received Date: July 30, 2022
  • Accepted Date: August 07, 2022
  • Available Online: December 29, 2022
  • Issue Publish Date: January 29, 2023
    Graphical Abstract
    • Abstract
  • Skin diseases affect people's health and quality of life because of their high incidence, difficult diagnosis and apparent harm, coupled with insufficient medical resources. In recent years, with the development of computer-aided diagnosis (CAD) technology, single-modality CAD approaches have broken the limitations of traditional methods, such as strong subjectivity, and high missed-diagnosis and misdiagnosis rate, but failed to leverage the multi-modal information in real clinical scenarios. Multi-modality CAD methods help artificial intelligence models learn the clinical representations in a more complex and comprehensive manner, aiding dermatologists in making a more accurate diagnosis of skin diseases. This article introduces different types of skin lesion data commonly used in CAD methods, summarizes the single-modality/multi-modality methods based on related works in the field of CAD systems of skin diseases, and predicts possible future development trends of CAD technology, thus providing insights for mitigating the challenge on the diagnosis of skin diseases.
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    • References (54)
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