{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:41:01Z","timestamp":1758361261609,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032053244"},{"type":"electronic","value":"9783032053251"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05325-1_3","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:10Z","timestamp":1758308710000},"page":"24-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Virtual Domain Collaborative Learning Framework for\u00a0Semi-supervised Microscopic Hyperspectral Image Segmentation"],"prefix":"10.1007","author":[{"given":"Geng","family":"Qin","sequence":"first","affiliation":[]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haihao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuxing","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"5","key":"3_CR1","doi-asserted-by":"publisher","first-page":"417","DOI":"10.2174\/1573405618666220519144358","volume":"19","author":"S Karim","year":"2022","unstructured":"Karim, S., Qadir, A., Farooq, U., Shakir, M., Laghari, A.A.: Hyperspectral Imaging: A Review and Trends Towards Medical Imaging. Curr Med Imaging 19(5), 417\u2013427 (2022)","journal-title":"Curr Med Imaging"},{"issue":"4","key":"3_CR2","doi-asserted-by":"publisher","first-page":"046001","DOI":"10.1117\/1.JMI.10.4.046001","volume":"10","author":"A Bahl","year":"2023","unstructured":"Bahl, A., et al.: Synthetic White Balancing for Intra-operative Hyperspectral Imaging. Journal of Medical Imaging 10(4), 046001\u2013046001 (2023)","journal-title":"Journal of Medical Imaging"},{"issue":"3","key":"3_CR3","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1111\/php.13725","volume":"99","author":"MA Calin","year":"2023","unstructured":"Calin, M.A., Manea, D., Savastru, R., Parasca, S.V.: Mapping the Distribution of Melanin Concentration in Different Fitzpatrick Skin Types Using Hyperspectral Imaging Technique. Photochem. Photobiol. 99(3), 1020\u20131027 (2023)","journal-title":"Photochem. Photobiol."},{"issue":"6","key":"3_CR4","doi-asserted-by":"publisher","first-page":"060503","DOI":"10.1117\/1.JBO.22.6.060503","volume":"22","author":"M Halicek","year":"2017","unstructured":"Halicek, M., Lu, G., Little, J.V., Wang, X., Patel, M., Griffith, C.C., El-Deiry, M.W., Chen, A.Y., Fei, B.: Deep Convolutional Neural Networks for Classifying Head and Neck Cancer Using Hyperspectral Imaging. J. Biomed. Opt. 22(6), 060503\u2013060503 (2017)","journal-title":"J. Biomed. Opt."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Q. Huang, W. Li, B. Zhang, Q. Li, R. Tao, and N. H. Lovell.: Blood cell classification based on hyperspectral imaging with modulated gabor and cnn. IEEE Journal of Biomedical and Health Informatics 24(1), 160\u2013170 (2020)","DOI":"10.1109\/JBHI.2019.2905623"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"W. Zeng, W. Li, M. Zhang, H. Wang, M. Lv, Y. Yang, and R. Tao.: Microscopic hyperspectral image classification based on fusion transformer with parallel cnn. IEEE Journal of Biomedical and Health Informatics 27(6), 2910\u20132921 (2023)","DOI":"10.1109\/JBHI.2023.3253722"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"X. Zhang, Q. Li, W. Li, Y. Guo, J. Zhang, C. Guo, K. Chang, and N. H. Lovell.: FD-net: Feature distillation network for oral squamous cell carcinoma lymph node segmentation in hyperspectral imagery. IEEE Journal of Biomedical and Health Informatics 28(3), 1552\u20131563 (2024)","DOI":"10.1109\/JBHI.2024.3350245"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"B. Yun, B. Lei, J. Chen, H. Wang, S. Qiu, W. Shen, Q. Li, and Y. Wang.: Spectr: Spectral transformer for microscopic hyperspectral pathology image segmentation. IEEE Transactions on Circuits and Systems for Video Technology 34(6), 4610\u20134624 (2024)","DOI":"10.1109\/TCSVT.2023.3326196"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, L.L., Gao, K., Hu, D., Feng, Z., Hou, C., Rong, P., Wang, W.: Ss-tbn: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)","DOI":"10.1109\/TPAMI.2023.3240886"},{"issue":"3","key":"3_CR10","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TMI.2022.3217501","volume":"42","author":"F Lyu","year":"2022","unstructured":"Lyu, F., Ye, M., Carlsen, J.F., Erleben, K., Darkner, S., Yuen, P.C.: Pseudo-label Guided Image Synthesis for Semi-supervised Covid-19 Pneumonia Infection Segmentation. IEEE Trans. Med. Imaging 42(3), 797\u2013809 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110020","volume":"146","author":"C Tang","year":"2024","unstructured":"Tang, C., Zeng, X., Zhou, L., Zhou, Q., Wang, P., Wu, X., Ren, H., Zhou, J., Wang, Y.: Semi-supervised Medical Image Segmentation via Hard Positives Oriented Contrastive Learning. Pattern Recogn. 146, 110020 (2024)","journal-title":"Pattern Recogn."},{"key":"3_CR12","unstructured":"K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, vol. 33, pp. 596\u2013608 (2020)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Zheng, M., You, S., Huang, L., Wang, F., Qian, C., Xu, C.: Simmatch: Semi-supervised learning with similarity matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 14471\u201314481 (2022)","DOI":"10.1109\/CVPR52688.2022.01407"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"L. Yang, L. Qi, L. Feng, W. Zhang, and Y. Shi.: Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7236\u20137246 (2023)","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"3_CR15","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems 30 (2017)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation. In: MICCAI. pp. 605\u2013613 (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, Z., Wu, Q., Ge, Z., Cai, J.: Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation. In: MICCAI. pp. 34\u201343 (2022)","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"3_CR18","unstructured":"J. Na, J.-W. Ha, H. J. Chang, D. Han, and W. Hwang.: Switching temporary teachers for semi-supervised semantic segmentation. Advances in Neural Information Processing Systems 36 (2023)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"X. Chen, Y. Yuan, G. Zeng, and J. Wang.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Y. Li, X. Wang, L. Yang, L. Feng, W. Zhang and Y. Gao.: Diverse Cotraining Makes Strong Semi-Supervised Segmentor. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 16009-16021 (2023)","DOI":"10.1109\/ICCV51070.2023.01471"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"H. Chi, J. Pang, B. Zhang, and W. Liu.: Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4070\u20134080 (2024)","DOI":"10.1109\/CVPR52733.2024.00390"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"L. Li, S. Lian, Z. Luo, B. Wang, S. Li.: VCLIPSeg: Voxel-wise CLIP-Enhanced model for Semi-Supervised Medical Image Segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 692-701 (2024)","DOI":"10.1007\/978-3-031-72114-4_66"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Q. Zhang, Q. Li, G. Yu, L. Sun, M. Zhou, and J. Chu.: A multidimensional choledoch database and benchmarks for cholangiocarcinoma diagnosis. IEEE Access 7, 149414\u2013149421 (2019)","DOI":"10.1109\/ACCESS.2019.2947470"},{"key":"3_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu, Y., Ge, Z., Zhang, D., Xu, M., Zhang, L., Xia, Y., Cai, J.: Mutual Consistency Learning for Semi-supervised Medical Image Segmentation. Med. Image Anal. 81, 102530 (2022)","journal-title":"Med. Image Anal."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"S. Yun, D. Han, S. Chun, S. J. Oh, Y. Yoo and J. Choe.: CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 6022-6031 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"V. Olsson, W. Tranheden, J. Pinto and L. Svensson.: ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1368-1377 (2021)","DOI":"10.1109\/WACV48630.2021.00141"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05325-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:18Z","timestamp":1758308718000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05325-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032053244","9783032053251"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05325-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}