{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:12:20Z","timestamp":1778080340251,"version":"3.51.4"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031966248","type":"print"},{"value":"9783031966255","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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\/978-3-031-96625-5_23","type":"book-chapter","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:09:17Z","timestamp":1754464157000},"page":"342-357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CoRLD: Contrastive Representation Learning of Deformable Shapes in Images"],"prefix":"10.1007","author":[{"given":"Tonmoy","family":"Hossain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaomiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"23_CR1","unstructured":"Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: International Conference on Machine Learning, pp. 40\u201349 (2018)"},{"key":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/11866565_113","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006","author":"V Arsigny","year":"2006","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A Log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924\u2013931. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866565_113"},{"issue":"1","key":"23_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Azad, R., et al.: Beyond self-attention: deformable large kernel attention for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1287\u20131297 (2024)","DOI":"10.1109\/WACV57701.2024.00132"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Azizi, S., et\u00a0al.: Big self-supervised models advance medical image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3478\u20133488 (2021)","DOI":"10.1109\/ICCV48922.2021.00346"},{"issue":"8","key":"23_CR6","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/B:VISI.0000043755.93987.aa","volume":"61","author":"MF Beg","year":"2005","unstructured":"Beg, M.F., Miller, M.I., Trouv\u00e9, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vision 61, 139\u2013157 (2005)","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Bhalodia, R., Elhabian, S.Y., Kavan, L., Whitaker, R.T.: DeepSSM: a deep learning framework for statistical shape modeling from raw images. In: Shape in Medical Imaging: International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018, Proceedings, pp. 244\u2013257. Springer (2018)","DOI":"10.1007\/978-3-030-04747-4_23"},{"key":"23_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102615","volume":"82","author":"J Chen","year":"2022","unstructured":"Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: transformer for unsupervised medical image registration. Med. Image Anal. 82, 102615 (2022)","journal-title":"Med. Image Anal."},{"key":"23_CR10","unstructured":"Christensen, G.E., Rabbitt, R.D., Miller, M.I., et\u00a0al.: A deformable neuroanatomy textbook based on viscous fluid mechanics. In: 27th Annual Conference on Information Sciences and Systems, pp. 211\u2013216. Citeseer (1993)"},{"issue":"1","key":"23_CR11","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/cviu.1995.1004","volume":"61","author":"TF Cootes","year":"1995","unstructured":"Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38\u201359 (1995)","journal-title":"Comput. Vis. Image Underst."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Xu, D., Li, W., Duan, L.: Harmonious teacher for cross-domain object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23829\u201323838 (2023)","DOI":"10.1109\/CVPR52729.2023.02282"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Hao, F., He, F., Liu, L., Wu, F., Tao, D., Cheng, J.: Class-aware patch embedding adaptation for few-shot image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 18905\u201318915 (2023)","DOI":"10.1109\/ICCV51070.2023.01733"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Hossain, T., Ma, J., Li, J., Zhang, M.: Invariant shape representation learning for image classification. arXiv preprint arXiv:2411.12201 (2024)","DOI":"10.1109\/WACV61041.2025.00420"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Hossain, T., Shishir, F.S., Ashraf, M., Al\u00a0Nasim, M.A., Shah, F.M.: Brain tumor detection using convolutional neural network. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICASERT.2019.8934561"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Hossain, T., Zhang, M.: MGAug: multimodal geometric augmentation in latent spaces of image deformations. Med. Image Anal., 103540 (2025)","DOI":"10.1016\/j.media.2025.103540"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"4","key":"23_CR19","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack, C.R., Jr., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685\u2013691 (2008)","journal-title":"J. Magn. Reson. Imaging"},{"key":"23_CR20","doi-asserted-by":"publisher","first-page":"S151","DOI":"10.1016\/j.neuroimage.2004.07.068","volume":"23","author":"S Joshi","year":"2004","unstructured":"Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151\u2013S160 (2004)","journal-title":"Neuroimage"},{"key":"23_CR21","unstructured":"Ke, T.-W., Mo, S., Stella, X.Y.: Learning hierarchical image segmentation for recognition and by recognition. In: The Twelfth International Conference on Learning Representations (2023)"},{"key":"23_CR22","unstructured":"Khosla, P., et al.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 18661\u201318673 (2020)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Kim, B., Han, I., Ye, J.C.: DiffuseMorph: unsupervised deformable image registration using diffusion model. In: European Conference on Computer Vision, pp. 347\u2013364. Springer (2022)","DOI":"10.1007\/978-3-031-19821-2_20"},{"key":"23_CR24","unstructured":"Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"23_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-87202-1_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"T Mok","year":"2021","unstructured":"Mok, T., Chung, A.: Conditional deformable image registration with convolutional neural network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 35\u201345. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_4"},{"issue":"10","key":"23_CR27","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1109\/42.811263","volume":"18","author":"SM Pizer","year":"1999","unstructured":"Pizer, S.M., Fritsch, D.S., Yushkevich, P.A., Johnson, V.E., Chaney, E.L.: Segmentation, registration, and measurement of shape variation via image object shape. IEEE Trans. Med. Imaging 18(10), 851\u2013865 (1999)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR28","unstructured":"Pu, Y., et al.: Rank-DETR for high quality object detection. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"issue":"4","key":"23_CR29","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1016\/j.neuroimage.2012.02.084","volume":"61","author":"M Reuter","year":"2012","unstructured":"Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402\u20131418 (2012)","journal-title":"Neuroimage"},{"issue":"8","key":"23_CR30","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/TMI.2003.815865","volume":"22","author":"D Rueckert","year":"2003","unstructured":"Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans. Med. Imaging 22(8), 1014\u20131025 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR31","unstructured":"Vilas, M.G., Schauml\u00f6ffel, T., Roig, G.: Analyzing vision transformers for image classification in class embedding space. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, M.: Geo-SIC: learning deformable geometric shapes in deep image classifiers. In: Advances in Neural Information Processing Systems, vol. 35, pp. 27994\u201328007 (2022)","DOI":"10.52202\/068431-2030"},{"issue":"1","key":"23_CR33","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S1361-8415(01)80004-9","volume":"1","author":"WM Wells III","year":"1996","unstructured":"Wells, W.M., III., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35\u201351 (1996)","journal-title":"Med. Image Anal."},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Wu, N., Zhang, M.: NeurEPDiff: neural operators to predict geodesics in deformation spaces. In: International Conference on Information Processing in Medical Imaging, pp. 588\u2013600. Springer (2023)","DOI":"10.1007\/978-3-031-34048-2_45"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"issue":"1","key":"23_CR36","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., et al.: MedMNIST V2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10(1), 41 (2023)","journal-title":"Sci. Data"},{"key":"23_CR37","unstructured":"Yang, X., Kwitt, R., Niethammer, M.: Fast predictive image registration. In: Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, 21 October 2016, Proceedings, vol. 1, pp. 48\u201357. Springer (2016)"},{"key":"23_CR38","unstructured":"You, C., et al.: Rethinking semi-supervised medical image segmentation: a variance-reduction perspective. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"23_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-319-59050-9_44","volume-title":"Information Processing in Medical Imaging","author":"M Zhang","year":"2017","unstructured":"Zhang, M., et al.: Frequency diffeomorphisms for efficient image registration. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 559\u2013570. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_44"},{"key":"23_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-642-38868-2_4","volume-title":"Information Processing in Medical Imaging","author":"M Zhang","year":"2013","unstructured":"Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Z\u00f6llei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37\u201348. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38868-2_4"},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543\u20138553 (2019)","DOI":"10.1109\/CVPR.2019.00874"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96625-5_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T13:20:47Z","timestamp":1777468847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96625-5_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9783031966248","9783031966255"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96625-5_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"7 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"25 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}