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Related Concept Videos

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Related Experiment Video

Updated: Nov 22, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort,

Xiangyu Tan1, Kexin Li1, Jiucheng Zhang2

  • 1Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.

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|January 8, 2021
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Summary

A deep learning model using ThinPrep cytologic test (TCT) images aids cervical cancer screening, improving speed and accuracy. This artificial intelligence tool assists pathologists, especially in resource-limited settings, for earlier diagnosis and prevention.

Keywords:
Cervical cancerConvolutional neural network (CNN)Deep leaningThinPrep cytologic test (TCT)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Cervical cancer incidence is rising in developing nations with limited medical resources.
  • Deep learning offers high-accuracy, rapid cancer screening for early diagnosis and effective treatment.
  • Developing a robust deep convolutional neural network (DCNN) model to assist pathologists in cervical cancer screening.

Purpose of the Study:

  • To construct a robust DCNN model for assisting pathologists in cervical cancer screening.
  • To evaluate the performance of a Faster R-CNN system for TCT image analysis.
  • To address the shortage of medical resources for cervical cancer screening.

Main Methods:

  • Collected ThinPrep cytologic test (TCT) images diagnosed by pathologists from multiple hospitals.
  • Utilized a training dataset of 13,775 images, a validation dataset of 2301 images, and a test dataset of 408,030 images.
  • Trained and evaluated a Faster R-CNN system for image classification and report generation.

Main Results:

  • The screening system achieved a sensitivity of 99.4% and specificity of 34.8% (AUC 0.67).
  • Model demonstrated sensitivity for ASCUS (89.3%), LSIL (71.5%), and HSIL (73.9%).
  • The system generated reports in approximately 3 minutes, reducing pathologist workload.

Conclusions:

  • A CNN-based TCT cervical-cancer screening model was developed using multicenter TCT images.
  • The model demonstrated improved speed and accuracy in cervical cancer screening.
  • This AI tool helps overcome medical resource limitations for cervical cancer screening.