{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:56:45Z","timestamp":1769842605292,"version":"3.49.0"},"reference-count":47,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100018806","name":"Science and Technology Department of Hubei Province","doi-asserted-by":"publisher","award":["2019AEA170"],"award-info":[{"award-number":["2019AEA170"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Cervical cancer (CC) seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a noninvasive, high\u2010resolution imaging technology for cervical disease detection. However, OCT image annotation is knowledge\u2010intensive and time\u2010consuming, which impedes the training process of deep\u2010learning\u2010based classification models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to develop a computer\u2010aided diagnosis (CADx) approach to classifying in\u2010vivo cervical OCT images based on self\u2010supervised learning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In addition to high\u2010level 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\u2019 texture features. We conducted 10\u2010fold cross\u2010validation on the OCT image dataset from a multicenter clinical study on 733 patients from China.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In a binary classification task for detecting high\u2010risk diseases, including high\u2010grade squamous intraepithelial lesion and CC, our method achieved an area\u2010under\u2010the\u2010curve value of 0.9798\u00a0\u00b1\u00a00.0157 with a sensitivity of 91.17%\u00a0\u00b1\u00a04.99% and a specificity of 93.96%\u00a0\u00b1\u00a04.72% 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\u2010dimensional OCT volumes from 118 Chinese patients in a new hospital using a cross\u2010shaped threshold voting strategy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed contrastive\u2010learning\u2010based CADx method outperformed the end\u2010to\u2010end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of \u201csee\u2010and\u2010treat.\u201d<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15630","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T08:58:40Z","timestamp":1648457920000},"page":"3638-3653","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Cervical optical coherence tomography image classification based on contrastive self\u2010supervised texture learning"],"prefix":"10.1002","volume":"49","author":[{"given":"Kaiyi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science Wuhan University  Wuhan People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingbin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science Wuhan University  Wuhan People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science Wuhan University  Wuhan People's Republic of 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