Semi-supervised Dual-teacher Comparative Learning with Bidirectional Bisect Copy-paste for Medical Image Segmentation

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dc.contributor.authorFang, Jiangxiongen_US
dc.contributor.authorQi, Shikuanen_US
dc.contributor.authorLiu, Huaxiangen_US
dc.contributor.authorFu, Youyaoen_US
dc.contributor.authorZhang, Shiqingen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:05:12Z
dc.date.available2025-10-07T06:05:12Z
dc.date.issued2025
dc.description.abstractSemi-supervised learning leverages limited pixel-level annotated data and abundant unlabeled data to achieve effective semantic image segmentation. To address this, we propose a semi-supervised learning framework, integrated with a bidirectional bisect copy-paste (B2P) mechanism. We introduce a B2CP strategy applied to labeled and unlabeled data in the second teacher network, preserving both data types to enhance training diversity. This mechanism, coupled with copy-paste-based supervision for the student network, effectively mitigates interference from uncontrollable regions. Extensive experiments on the ACDC public datasets demonstrate the efficiency of the proposed model. It surpasses the fully supervised U-Net at a 5% labeled data and 20% labeled data.en_US
dc.description.sectionheadersPosters and Demos
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251304
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251304
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251304
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectImage segmentation;Semi-supervised learning; Bidirectional Copy-paste method
dc.subjectImage segmentation
dc.subjectSemi
dc.subjectsupervised learning
dc.subjectBidirectional Copy
dc.subjectpaste method
dc.titleSemi-supervised Dual-teacher Comparative Learning with Bidirectional Bisect Copy-paste for Medical Image Segmentationen_US
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