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 () and Xiuli Liu ()
Additional contact information
Shenghua Cheng: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Sibo Liu: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Jingya Yu: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Gong Rao: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Yuwei Xiao: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Wei Han: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Wenjie Zhu: Tongji Medical College, Huazhong University of Science and Technology
Xiaohua Lv: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Ning Li: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Jing Cai: Huazhong University of Science and Technology
Zehua Wang: Huazhong University of Science and Technology
Xi Feng: Huazhong University of Science and Technology
Fei Yang: Huazhong University of Science and Technology
Xiebo Geng: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Jiabo Ma: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Xu Li: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Ziquan Wei: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Xueying Zhang: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Tingwei Quan: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Shaoqun Zeng: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology
Li Chen: Huazhong University of Science and Technology
Junbo Hu: Tongji Medical College, Huazhong University of Science and Technology
Xiuli Liu: Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.

Date: 2021
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DOI: 10.1038/s41467-021-25296-x

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