Robust whole slide image analysis for cervical cancer screening using deep learning. 2021

Shenghua Cheng, and Sibo Liu, and Jingya Yu, and Gong Rao, and Yuwei Xiao, and Wei Han, and Wenjie Zhu, and Xiaohua Lv, and Ning Li, and Jing Cai, and Zehua Wang, and Xi Feng, and Fei Yang, and Xiebo Geng, and Jiabo Ma, and Xu Li, and Ziquan Wei, and Xueying Zhang, and Tingwei Quan, and Shaoqun Zeng, and Li Chen, and Junbo Hu, and Xiuli Liu
Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, China.

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.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D002583 Uterine Cervical Neoplasms Tumors or cancer of the UTERINE CERVIX. Cancer of Cervix,Cancer of the Cervix,Cancer of the Uterine Cervix,Cervical Cancer,Cervical Neoplasms,Cervix Cancer,Cervix Neoplasms,Neoplasms, Cervical,Neoplasms, Cervix,Uterine Cervical Cancer,Cancer, Cervical,Cancer, Cervix,Cancer, Uterine Cervical,Cervical Cancer, Uterine,Cervical Cancers,Cervical Neoplasm,Cervical Neoplasm, Uterine,Cervix Neoplasm,Neoplasm, Cervix,Neoplasm, Uterine Cervical,Uterine Cervical Cancers,Uterine Cervical Neoplasm
D003581 Cytodiagnosis Diagnosis of the type and, when feasible, the cause of a pathologic process by means of microscopic study of cells in an exudate or other form of body fluid. (Stedman, 26th ed) Cytodiagnoses
D003936 Diagnosis, Computer-Assisted Application of computer programs designed to assist the physician in solving a diagnostic problem. Computer-Assisted Diagnosis,Computer Assisted Diagnosis,Computer-Assisted Diagnoses,Diagnoses, Computer-Assisted,Diagnosis, Computer Assisted
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000077321 Deep Learning Supervised or unsupervised machine learning methods that use multiple layers of data representations generated by nonlinear transformations, instead of individual task-specific ALGORITHMS, to build and train neural network models. Hierarchical Learning,Learning, Deep,Learning, Hierarchical
D012372 ROC Curve A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli. ROC Analysis,Receiver Operating Characteristic,Analysis, ROC,Analyses, ROC,Characteristic, Receiver Operating,Characteristics, Receiver Operating,Curve, ROC,Curves, ROC,ROC Analyses,ROC Curves,Receiver Operating Characteristics
D015203 Reproducibility of Results The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results. Reliability and Validity,Reliability of Result,Reproducibility Of Result,Reproducibility of Finding,Validity of Result,Validity of Results,Face Validity,Reliability (Epidemiology),Reliability of Results,Reproducibility of Findings,Test-Retest Reliability,Validity (Epidemiology),Finding Reproducibilities,Finding Reproducibility,Of Result, Reproducibility,Of Results, Reproducibility,Reliabilities, Test-Retest,Reliability, Test-Retest,Result Reliabilities,Result Reliability,Result Validities,Result Validity,Result, Reproducibility Of,Results, Reproducibility Of,Test Retest Reliability,Validity and Reliability,Validity, Face
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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