Deep Learning for Computational Cytology: A Survey

• URL: http://arxiv.org/abs/2202.05126v1
• Date: Thu, 10 Feb 2022 16:22:10 GMT
• Title: Deep Learning for Computational Cytology: A Survey
• Authors: Hao Jiang, Yanning Zhou, Yi Lin, Ronald CK Chan, Jiang Liu, Hao Chen
• Abstract summary: We introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize the public datasets, evaluation metrics, versatile cyto image analysis applications including classification, detection, segmentation, and other related tasks.
• Score: 12.08083533402352
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) algorithms have made significant progress in medical image analysis, leading to the boosting publications of cytological studies. To investigate the advanced methods and comprehensive applications, we survey more than 120 publications of DL-based cytology image analysis in this article. We first introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize the public datasets, evaluation metrics, versatile cytology image analysis applications including classification, detection, segmentation, and other related tasks. Finally, we discuss current challenges and potential research directions of computational cytology.

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