Deep learning for computational cytology: A survey

Hao Jiang, Yanning Zhou, Yi Lin, Ronald C.K. Chan, Jiang Liu, Hao Chen

Research output: Journal PublicationReview articlepeer-review

6 Citations (Scopus)


Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.

Original languageEnglish
Article number102691
JournalMedical Image Analysis
Publication statusPublished - Feb 2023
Externally publishedYes


  • Artificial intelligence
  • Cancer screening
  • Computational cytology
  • Deep learning
  • Pathology
  • Survey

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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