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Updated: Sep 28, 2025

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Cervical optical coherence tomography image classification based on contrastive self-supervised texture learning.

Kaiyi Chen1, Qingbin Wang1, Yutao Ma1

  • 1School of Computer Science, Wuhan University, Wuhan, People's Republic of China.

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|March 28, 2022
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Summary

This study introduces a self-supervised learning method for classifying cervical OCT images, achieving high accuracy in detecting high-risk diseases and outperforming human experts. This approach enhances cervical cancer diagnosis and shows promise for clinical "see-and-treat" protocols.

Keywords:
cervical cancerlocal binary patternoptical coherence tomographyself-supervised learningvisualization

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Cervical cancer (CC) poses a significant threat to women's reproductive health.
  • Optical coherence tomography (OCT) offers noninvasive, high-resolution imaging for cervical disease detection.
  • Manual annotation of OCT images is labor-intensive, hindering deep learning model development.

Purpose of the Study:

  • To develop a computer-aided diagnosis (CADx) system for classifying in-vivo cervical OCT images.
  • To utilize self-supervised learning for enhanced OCT image analysis.

Main Methods:

  • A convolutional neural network (CNN) extracted semantic features.
  • A contrastive texture learning strategy was employed to leverage unlabeled OCT image texture features.
  • 10-fold cross-validation was performed on a multicenter dataset of 733 patients.

Main Results:

  • Achieved an Area Under the Curve (AUC) of 0.9798 ± 0.0157 for binary classification of high-risk lesions and CC.
  • Demonstrated high sensitivity (91.17% ± 4.99%) and specificity (93.96% ± 4.72%) on OCT image patches.
  • Outperformed two out of four medical experts and validated on an external dataset with 91.53% sensitivity and 97.37% specificity.

Conclusions:

  • The proposed contrastive-learning-based CADx method surpasses end-to-end CNN models.
  • The approach offers improved interpretability through texture feature analysis.
  • This method shows significant potential for integration into clinical 'see-and-treat' protocols for cervical cancer.