Nature Communications (Sep 2021)
Robust whole slide image analysis for cervical cancer screening using deep learning
- Shenghua Cheng,
- Sibo Liu,
- Jingya Yu,
- Gong Rao,
- Yuwei Xiao,
- Wei Han,
- Wenjie Zhu,
- Xiaohua Lv,
- Ning Li,
- Jing Cai,
- Zehua Wang,
- Xi Feng,
- Fei Yang,
- Xiebo Geng,
- Jiabo Ma,
- Xu Li,
- Ziquan Wei,
- Xueying Zhang,
- Tingwei Quan,
- Shaoqun Zeng,
- Li Chen,
- Junbo Hu,
- Xiuli Liu
Affiliations
- Shenghua Cheng
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Sibo Liu
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Jingya Yu
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Gong Rao
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Yuwei Xiao
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Wei Han
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Wenjie Zhu
- Department of Pathology, Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology
- Xiaohua Lv
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Ning Li
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Jing Cai
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Zehua Wang
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Xi Feng
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Fei Yang
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Xiebo Geng
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Jiabo Ma
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Xu Li
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Ziquan Wei
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Xueying Zhang
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Tingwei Quan
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Shaoqun Zeng
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- Li Chen
- Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Junbo Hu
- Department of Pathology, Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology
- Xiuli Liu
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
- DOI
- https://doi.org/10.1038/s41467-021-25296-x
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 10
Abstract
Computer-assisted diagnosis is key for scaling up cervical cancer screening, but current algorithms perform poorly on whole slide image analysis and generalization. Here, the authors present a WSI classification and top lesion cell recommendation system using deep learning, and achieve comparable results with cytologists.
