{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:23:55Z","timestamp":1778257435038,"version":"3.51.4"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049261","type":"print"},{"value":"9783032049278","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04927-8_9","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:09:07Z","timestamp":1758388147000},"page":"87-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["C2MAOT: Cross-modal Complementary Masked Autoencoder with\u00a0Optimal Transport for\u00a0Cancer Segmentation in\u00a0PET-CT Images"],"prefix":"10.1007","author":[{"given":"Jiaju","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaobin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinglong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoneng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Aide, N., Lasnon, C., et\u00a0al.: Advances in pet\/ct technology: an update. In: Seminars in nuclear medicine. vol.\u00a052, pp. 286\u2013301. Elsevier (2022)","DOI":"10.1053\/j.semnuclmed.2021.10.005"},{"key":"9_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105253","volume":"144","author":"MA Azam","year":"2022","unstructured":"Azam, M.A., et al.: A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput. Biol. Med. 144, 105253 (2022)","journal-title":"Comput. Biol. Med."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., et\u00a0al.: Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis (2024)","DOI":"10.1016\/j.media.2024.103280"},{"issue":"9","key":"9_CR5","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2023.3260990","volume":"42","author":"S Chen","year":"2023","unstructured":"Chen, S., Wu, Z., Li, M., Zhu, Y., Xie, H., Yang, P., Zhao, C., Zhang, Y., Zhang, S., Zhao, X., et al.: Fit-net: Feature interaction transformer network for pathologic myopia diagnosis. IEEE Trans. Med. Imaging 42(9), 2524\u20132538 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"9_CR6","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s13167-024-00363-7","volume":"15","author":"S Chen","year":"2024","unstructured":"Chen, S., Zhao, X., Wu, Z., Cao, K., Zhang, Y., Tan, T., Lam, C.T., Xu, Y., Zhang, G., Sun, Y.: Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity. EPMA Journal 15(2), 261\u2013274 (2024)","journal-title":"EPMA Journal"},{"key":"9_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597\u20131607. PmLR (2020)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 9640\u20139649 (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Agarwal, D., Aggarwal, K., Safta, W., et\u00a0al.: Masked image modeling advances 3d medical image analysis. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 1970\u20131980 (2023)","DOI":"10.1109\/WACV56688.2023.00201"},{"key":"9_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Gao, Y., Tan, T., et\u00a0al.: Multi-modal longitudinal representation learning for predicting neoadjuvant therapy response in breast cancer treatment. IEEE Journal of Biomedical and Health Informatics (2025)","DOI":"10.1109\/JBHI.2025.3540574"},{"issue":"1","key":"9_CR12","first-page":"601","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body fdg-pet\/ct dataset with manually annotated tumor lesions sci. Data 9(1), 601 (2022)","journal-title":"Data"},{"key":"9_CR13","first-page":"21271","volume":"33","author":"JB Grill","year":"2020","unstructured":"Grill, J.B., Strub, F., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Huang, J., Chen, S., Liang, X., Sun, Y., Hu, M., Tan, T.: All-in-one multi-organ segmentation in 3d ct images via self-supervised and cross-dataset learning. In: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). pp.\u00a01\u20135. IEEE (2025)","DOI":"10.1109\/ISBI60581.2025.10980981"},{"issue":"1","key":"9_CR18","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41746-023-00811-0","volume":"6","author":"SC Huang","year":"2023","unstructured":"Huang, S.C., Pareek, A., Jensen, M., Lungren, M.P., Yeung, S., Chaudhari, A.S.: Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digital Medicine 6(1), 74 (2023)","journal-title":"NPJ Digital Medicine"},{"key":"9_CR19","unstructured":"Huang, Z., Wang, H., Deng, Z., Ye, J., Su, Y., Sun, H., He, J., et\u00a0al.: Stu-net: Scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training. arXiv preprint arXiv:2304.06716 (2023)"},{"issue":"2","key":"9_CR20","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"8","key":"9_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101733","volume":"35","author":"SU Khan","year":"2023","unstructured":"Khan, S.U., Khan, M.A., Azhar, M., Khan, F., Lee, Y., Javed, M.: Multimodal medical image fusion towards future research: A review. Journal of King Saud University-Computer and Information Sciences 35(8), 101733 (2023)","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"9_CR22","unstructured":"Lee, H.H., Bao, S., Huo, Y., Landman, B.A.: 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076 (2022)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Lu, J., Chen, J., Cai, L., Jiang, S., Zhang, Y.: H2aseg: Hierarchical adaptive interaction and weighting network for tumor segmentation in pet\/ct images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 316\u2013327. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_30"},{"key":"9_CR24","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"9_CR25","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.3389\/fonc.2020.01301","volume":"10","author":"Y Ming","year":"2020","unstructured":"Ming, Y., Wu, N., et al.: Progress and future trends in pet\/ct and pet\/mri molecular imaging approaches for breast cancer. Front. Oncol. 10, 1301 (2020)","journal-title":"Front. Oncol."},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Montesuma, E.F., Mboula, F.M.N., Souloumiac, A.: Recent advances in optimal transport for machine learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","DOI":"10.1109\/TPAMI.2024.3489030"},{"issue":"1","key":"9_CR27","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1146\/annurev-statistics-030718-104938","volume":"6","author":"VM Panaretos","year":"2019","unstructured":"Panaretos, V.M., Zemel, Y.: Statistical aspects of wasserstein distances. Annual review of statistics and its application 6(1), 405\u2013431 (2019)","journal-title":"Annual review of statistics and its application"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Tang, Y., Yang, D., et\u00a0al.: Self-supervised pre-training of swin transformers for 3d medical image analysis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 20730\u201320740 (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, Z., et\u00a0al.: Swinmm: masked multi-view with swin transformers for 3d medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. pp. 486\u2013496. Springer (2023)","DOI":"10.1007\/978-3-031-43898-1_47"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhuang, J., Chen, H.: Voco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 22873\u201322882 (2024)","DOI":"10.1109\/CVPR52733.2024.02158"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9653\u20139663 (2022)","DOI":"10.1109\/CVPR52688.2022.00943"},{"issue":"1","key":"9_CR32","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s41523-023-00517-2","volume":"9","author":"T Zhang","year":"2023","unstructured":"Zhang, T., Tan, T., et al.: Predicting breast cancer types on and beyond molecular level in a multi-modal fashion. NPJ breast cancer 9(1), 16 (2023)","journal-title":"NPJ breast cancer"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Han, L., Sun, Y., Tan, T., et\u00a0al.: Unimrisegnet: Universal 3d network for various organs and cancers segmentation on multi-sequence mri. IEEE Journal of Biomedical and Health Informatics (2024)","DOI":"10.1109\/JBHI.2024.3504603"}],"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\/978-3-032-04927-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:09:16Z","timestamp":1758388156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04927-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049261","9783032049278"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04927-8_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","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"}}]}}