Background Cervical cancer (CC) seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a noninvasive, 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 designs a contrastive texture learning strategy to leverage unlabeled cervical OCT images’ texture features. We conducted 10‐fold cross‐validation on the OCT image dataset from a multicenter 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 CC, our method achieved an area‐under‐the‐curve value of 0.9798 ± 0.0157 with a sensitivity of 91.17% ± 4.99% and a specificity of 93.96% ± 4.72% for OCT image patches; also, it outperformed two out of four medical experts on the test set. Furthermore, our method achieved 91.53% sensitivity and 97.37% specificity on an external validation dataset containing 287 three‐dimensional 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.”

No abstract