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Cervical optical coherence tomography image classification based on contrastive self-supervised texture learning
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DOI：10.1002/mp.15630.png)
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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.
Keyword:
cervical cancer
local binary pattern
optical coherence tomography
self-supervised learning
visualization
期刊
IF:
3.2
论文数:
3.7W
被引数:
3.2W
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