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Review | Publishing Language: Chinese

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

Yiguang YANG1Juncheng WANG2,3Fengying XIE1( )Jie LIU2,3( )
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
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
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
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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.

CLC number: R751;R44 Document code: A Article ID: 1674-9081(2023)01-0168-09

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Medical Journal of Peking Union Medical College Hospital
Pages 168-176

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Cite this article:
YANG Y, WANG J, XIE F, et al. Data and Methods in Computer-aided Diagnosis Systems of Skin Diseases. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 168-176. https://doi.org/10.12290/xhyxzz.2022-0413

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Received: 31 July 2022
Accepted: 08 August 2022
Published: 30 December 2022
© 2024 Medical Journal of Peking Union Medical College Hospital