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However, most of the existing teacher\u2010student frameworks are prone to suffer from confirmation bias during training, adversely affecting the performance of SSMIS. To address this challenge, we propose the Dual Student Discrepancy Correction framework (DSDC), which extends the Mean Teacher (MT) framework by incorporating an additional student model with identical architecture but independently updated parameters. This design mitigates the parameter coupling issue that may arise when updating the teacher model via Exponential Moving Average (EMA) in conventional single\u2010student paradigms. Moreover, the prediction discrepancy between the two student models is leveraged for error detection and correction, enabling the network to identify and rectify its own cognitive biases, ultimately enhancing segmentation accuracy. Comprehensive experiments on two public benchmarks, an MRI dataset (LA) and a CT dataset (Pancreas\u2010NIH), reveal that our DSDC framework surpasses current State\u2010of\u2010the\u2010Art (SOTA) approaches across all evaluation metrics. These findings substantiate the framework's effectiveness in SSMIS tasks. Code is accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Sangfugui\/DSDC\">https:\/\/github.com\/Sangfugui\/DSDC<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/exsy.70285","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:55:58Z","timestamp":1778810158000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual Student Discrepancy Correction for Semi\u2010Supervised Medical Image Segmentation"],"prefix":"10.1111","volume":"43","author":[{"given":"Zhenfu","family":"Sang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Northeastern University  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"General Hospital of Northern Theatre Command  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-744X","authenticated-orcid":false,"given":"Chong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northeastern University  Shenyang China"},{"name":"Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education Northeastern University  Shenyang China"},{"name":"Engineering Research Center of Security Technology of Complex Network System, Ministry of Education Northeastern University  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering Beijing Information Science and Technology University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiu\u2010Wing","family":"Sham","sequence":"additional","affiliation":[{"name":"School of Computer Science University of Auckland  Auckland New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"e_1_2_10_3_1","first-page":"11514","article-title":"Bidirectional Copy\u2010Paste for Semi\u2010Supervised Medical Image Segmentation","author":"Bai Y.","year":"2023","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"e_1_2_10_4_1","first-page":"5050","article-title":"Mixmatch: A Holistic Approach to Semi\u2010Supervised Learning","volume":"32","author":"Berthelot D.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_5_1","first-page":"205","volume-title":"European Conference on Computer Vision","author":"Cao H.","year":"2022"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2023.3247433"},{"key":"e_1_2_10_7_1","unstructured":"Chen J. 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