  Background: Cervical cancer seriously affects the health of the female
reproductive system. Optical coherence tomography (OCT) emerged as a
non-invasive, high-resolution imaging technology for cervical disease
detection. However, OCT image annotation is knowledge-intensive and
time-consuming, which impedes the training process of deep-learning-based
classification models. Purpose: This study aims to develop a computer-aided
diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on
self-supervised learning. Methods: In addition to high-level semantic features
extracted by a convolutional neural network (CNN), the proposed CADx approach
leverages unlabeled cervical OCT images' texture features learned by
contrastive texture learning. We conducted ten-fold cross-validation on the OCT
image dataset from a multi-center clinical study on 733 patients from China.
Results: In a binary classification task for detecting high-risk diseases,
including high-grade squamous intraepithelial lesion and cervical cancer, our
method achieved an area-under-the-curve value of 0.9798 plus or minus 0.0157
with a sensitivity of 91.17 plus or minus 4.99% and a specificity of 93.96 plus
or minus 4.72% for OCT image patches; also, it outperformed two out of four
medical experts on the test set. Furthermore, our method achieved a 91.53%
sensitivity and 97.37% specificity on an external validation dataset containing
287 3D OCT volumes from 118 Chinese patients in a new hospital using a
cross-shaped threshold voting strategy. Conclusions: The proposed
contrastive-learning-based CADx method outperformed the end-to-end CNN models
and provided better interpretability based on texture features, which holds
great potential to be used in the clinical protocol of "see-and-treat."
