A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis

Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of compute...

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Bibliographic Details
Published inThe Artificial intelligence review Vol. 56; no. Suppl 2; pp. 2687 - 2758
Main Authors Jiang, Peng, Li, Xuekong, Shen, Hui, Chen, Yuqi, Wang, Lang, Chen, Hua, Feng, Jing, Liu, Juan
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.11.2023
Springer Nature B.V
Subjects
Artificial Intelligence
Cancer
Cellular biology
Cervical cancer
Computer Science
Cytology
Deep learning
Diagnosis
Image analysis
Image segmentation
Machine learning
Medical imaging
Systematic review
Cervical cytology
Deep learning
Cytopathology
Artificial intelligence
Cancer screening
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Summary:Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 80 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future research in automated cervical cytology screening.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10588-z