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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew\u2019s correlation coefficient (MCC), and Cohen\u2019s kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models\u2019 interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen\u2019s kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.<\/jats:p>","DOI":"10.1007\/s10278-024-01123-9","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T18:01:46Z","timestamp":1713895306000},"page":"2342-2353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification"],"prefix":"10.1007","volume":"37","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haoxiang","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Jielu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Lihe","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiaxi","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Minyue","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Jingwen","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Shiqi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-9248","authenticated-orcid":false,"given":"Jinzhou","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"1123_CR1","doi-asserted-by":"crossref","unstructured":"Fang S, et al.: Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study. 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