Semantic Scholar Open Access 2022 102 sitasi

Deep Learning for Computational Cytology: A Survey

Hao Jiang Yanning Zhou Yi Lin Ronald C. K. Chan Jiangshu Liu +1 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Engineering Medicine

Penulis (6)

Hao Jiang

Yanning Zhou

Yi Lin

Ronald C. K. Chan

Jiangshu Liu

Hao Chen

Format Sitasi

APA MLA BibTeX

Jiang, H., Zhou, Y., Lin, Y., Chan, R.C.K., Liu, J., Chen, H. (2022). Deep Learning for Computational Cytology: A Survey. https://doi.org/10.1016/j.media.2022.102691

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.media.2022.102691

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 102×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.media.2022.102691
Akses: Open Access ✓