{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:38:57Z","timestamp":1790221137021,"version":"4.1.0"},"reference-count":66,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program of Chongqing Technology Innovation and Application Development Special Project","award":["CSTB2022TIAD-KPX0161"],"award-info":[{"award-number":["CSTB2022TIAD-KPX0161"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["CSTB2024NSCQ-LZX0141"],"award-info":[{"award-number":["CSTB2024NSCQ-LZX0141"]}]},{"name":"Chongqing Excellence Program for Innovation and Entrepreneurship Leadership Talent Project","award":["CQYC20220303576"],"award-info":[{"award-number":["CQYC20220303576"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["62471293"],"award-info":[{"award-number":["62471293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Inflammatory bowel disease (IBD) includes Crohn\u2019s disease (CD) and ulcerative colitis (UC). The accurate classification of IBD from colonoscopy images is critical for diagnosis and treatment. However, the lack of labeled data poses a major challenge for developing deep learning-based IBD classification approaches. Recently, pseudo-labeling-based semi-supervised learning methods offer a promising solution in leveraging both labeled and unlabeled data to improve classification performance. Nevertheless, due to significant intra-class variability and the subtle inter-class differences in IBD colonoscopy images, pseudo-labels are often inaccurate, which results in confirmation bias and suboptimal performance. To address this challenge, a Subclass-Aware Contrastive Semi-Supervised Learning method, referred to as SACSSL, is proposed for accurate IBD classification by integrating a subclass-aware contrastive module into a pseudo-labeling-based semi-supervised framework, e.g., FixMatch. Specifically, unlabeled samples are first partitioned into confident and uncertain samples according to the confidence of pseudo-labels. An instance-level contrastive loss is then applied to uncertain samples, aiming to mitigate confirmation bias. Furthermore, intra-class heterogeneity is captured by introducing a set of prototypes for each subclass and assigning confident samples to these prototypes to form fine-grained subclasses, and supervised contrastive loss is applied to promote intra-subclass clustering, thereby enhancing inter-class separability while preserving intra-class diversity. Our method is evaluated on two datasets, i.e., an in-house collected Daping dataset for IBD classification and a publicly available LIMUC dataset for UC severity grading. On both datasets, our method achieves state-of-the-art performance under the semi-supervised setting. Specifically, with only 20% labeled data, the proposed method reaches an overall accuracy of 93.2% and an F1-score of 80.1% on the Daping dataset, which is close to the fully supervised upper bound (94.0% accuracy and 80.8% F1-score), and it achieves an overall accuracy of 76.4% and an F1-score of 68.9% on the LIMUC dataset. Comprehensive experimental results demonstrate the effectiveness of our method for semi-supervised colonoscopy image classification.<\/jats:p>","DOI":"10.3390\/bioengineering13010008","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:27:51Z","timestamp":1766586471000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Kechen","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China"},{"name":"Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangcong","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyang","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjian","family":"Nian","sequence":"additional","affiliation":[{"name":"Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanling","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-0379","authenticated-orcid":false,"given":"Guoyan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/S0140-6736(07)60750-8","article-title":"Inflammatory bowel disease: Cause and immunobiology","volume":"369","author":"Baumgart","year":"2007","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1016\/S0140-6736(17)32448-0","article-title":"Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: A systematic review of population-based studies","volume":"390","author":"Ng","year":"2017","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1038\/s41586-025-08940-0","article-title":"Global evolution of inflammatory bowel disease across epidemiologic stages","volume":"642","author":"Hracs","year":"2025","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1093\/ecco-jcc\/jjx009","article-title":"Third European evidence-based consensus on diagnosis and management of ulcerative colitis. 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