{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:59:13Z","timestamp":1776887953992,"version":"3.51.2"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723834","type":"print"},{"value":"9783031723841","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72384-1_16","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:02:53Z","timestamp":1727866973000},"page":"163-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DRIM: Learning Disentangled Representations from\u00a0Incomplete Multimodal Healthcare Data"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Robinet","sequence":"first","affiliation":[]},{"given":"Ahmad","family":"Berjaoui","sequence":"additional","affiliation":[]},{"given":"Ziad","family":"Kheil","sequence":"additional","affiliation":[]},{"given":"Elizabeth","family":"Cohen-Jonathan Moyal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Antolini, L., Boracchi, P., Biganzoli, E.: A time-dependent discrimination index for survival data. Statistics in Medicine 24(24) (2005)","DOI":"10.1002\/sim.2427"},{"issue":"3","key":"16_CR2","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1148\/radiol.2018180200","volume":"289","author":"S Bae","year":"2018","unstructured":"Bae, S., Choi, Y.S., Ahn, S.S., Chang, J.H., Kang, S.G., Kim, E.H., Kim, S.H., Lee, S.K.: Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology 289(3), 797\u2013806 (Dec 2018)","journal-title":"Radiology"},{"key":"16_CR3","unstructured":"Baid, U.e.a.: The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (Sep 2021), arXiv:2107.02314 [cs]"},{"key":"16_CR4","unstructured":"Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA (Oct 2017), arXiv:1710.05050 [stat]"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Braman, N., Gordon, J.W.H., Goossens, E.T., Willis, C., Stumpe, M.C., Venkataraman, J.: Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021. pp. 667\u2013677 (2021)","DOI":"10.1007\/978-3-030-87240-3_64"},{"key":"16_CR6","unstructured":"Cardoso, M.e.a.: MONAI: An open-source framework for deep learning in healthcare (Nov 2022), arXiv:2211.02701 [cs]"},{"issue":"10","key":"16_CR7","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"K Chang","year":"2013","unstructured":"Chang, K., The Cancer Genome Atlas Research Network, Genome Characterization Center, Genome Data Analysis Center, Sequencing Center, Data Coordinating Center, Tissue Source Site, Biospecimen Core Resource Center: The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics 45(10), 1113\u20131120 (Oct 2013)","journal-title":"Nature Genetics"},{"issue":"14","key":"16_CR8","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"A Cheerla","year":"2019","unstructured":"Cheerla, A., Gevaert, O.: Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446\u2013i454 (Jul 2019)","journal-title":"Bioinformatics"},{"key":"16_CR9","unstructured":"Chen, R.J., Lu, M.Y., Wang, J., Williamson, D.F.K., Rodig, S.J., Lindeman, N.I., Mahmood, F.: Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (Sep 2020), arXiv:1912.08937 [cs, q-bio]"},{"key":"16_CR10","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Contrastive Learning of Visual Representations. In: Proceedings of the 37th International Conference on Machine Learning. pp. 1597\u20131607. PMLR (Nov 2020), iSSN: 2640-3498"},{"key":"16_CR11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Oct 2020)"},{"key":"16_CR12","doi-asserted-by":"publisher","unstructured":"Gallego, O.: Nonsurgical treatment of recurrent glioblastoma. Current Oncology (Toronto, Ont.) 22(4), e273\u2013281 (Aug 2015). https:\/\/doi.org\/10.3747\/co.22.2436","DOI":"10.3747\/co.22.2436"},{"key":"16_CR13","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6257","volume":"7","author":"MF Gensheimer","year":"2019","unstructured":"Gensheimer, M.F., Narasimhan, B.: A scalable discrete-time survival model for neural networks. PeerJ 7, e6257 (Jan 2019)","journal-title":"PeerJ"},{"issue":"6","key":"16_CR14","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1002\/bimj.200610301","volume":"48","author":"TA Gerds","year":"2006","unstructured":"Gerds, T.A., Schumacher, M.: Consistent Estimation of the Expected Brier Score in General Survival Models with Right-Censored Event Times. Biometrical Journal 48(6), 1029\u20131040 (2006)","journal-title":"Biometrical Journal"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Girdhar, R., El-Nouby, A., Liu, Z., Singh, M., Alwala, K.V., Joulin, A., Misra, I.: ImageBind: One Embedding Space To Bind Them All. pp. 15180\u201315190 (2023)","DOI":"10.1109\/CVPR52729.2023.01457"},{"key":"16_CR16","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Nets. In: Advances in Neural Information Processing Systems. vol.\u00a027 (2014)"},{"issue":"17\u201318","key":"16_CR17","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5","volume":"18","author":"E Graf","year":"1999","unstructured":"Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M.: Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 18(17-18), 2529\u20132545 (1999)","journal-title":"Statistics in Medicine"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Han, W., Chen, H., Poria, S.: Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis (Sep 2021), arXiv:2109.00412 [cs]","DOI":"10.18653\/v1\/2021.emnlp-main.723"},{"key":"16_CR19","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 (CVPR) (June 2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR20","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus) (2023)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhan, X., Xiang, S., Johnson, T.S., Helm, B., Yu, C.Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., Huang, K.: SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer. Frontiers in Genetics 10 (2019)","DOI":"10.3389\/fgene.2019.00166"},{"key":"16_CR22","unstructured":"Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning. In: Advances in Neural Information Processing Systems. vol.\u00a033, pp. 18661\u201318673 (2020)"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Kvamme, H., Borgan, .: Continuous and discrete-time survival prediction with neural networks. Lifetime Data Analysis 27(4), 710\u2013736 (Oct 2021)","DOI":"10.1007\/s10985-021-09532-6"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Louis, D.N., Perry, A., Wesseling, P., Brat, D.J., Cree, I.A., Figarella-Branger, D., Hawkins, C., Ng, H.K., Pfister, S.M., Reifenberger, G., Soffietti, R., von Deimling, A., Ellison, D.W.: The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology 23(8), 1231\u20131251 (Aug 2021)","DOI":"10.1093\/neuonc\/noab106"},{"key":"16_CR25","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation Learning with Contrastive Predictive Coding (Jan 2019), arXiv:1807.03748 [cs, stat]"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Sanchez, E.H., Serrurier, M., Ortner, M.: Learning Disentangled Representations via Mutual Information Estimation. In: Computer Vision \u2013 ECCV 2020. pp. 205\u2013221 (2020)","DOI":"10.1007\/978-3-030-58542-6_13"},{"issue":"1","key":"16_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43856-023-00276-y","volume":"3","author":"S Steyaert","year":"2023","unstructured":"Steyaert, S., Qiu, Y.L., Zheng, Y., Mukherjee, P., Vogel, H., Gevaert, O.: Multimodal deep learning to predict prognosis in adult and pediatric brain tumors. Communications Medicine 3(1), 1\u201315 (Mar 2023)","journal-title":"Communications Medicine"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Tishby, N., Zaslavsky, N.: Deep Learning and the Information Bottleneck Principle (Mar 2015), arXiv:1503.02406 [cs]","DOI":"10.1109\/ITW.2015.7133169"},{"issue":"1","key":"16_CR29","doi-asserted-by":"publisher","first-page":"13505","DOI":"10.1038\/s41598-021-92799-4","volume":"11","author":"LA Vale-Silva","year":"2021","unstructured":"Vale-Silva, L.A., Rohr, K.: Long-term cancer survival prediction using multimodal deep learning. Scientific Reports 11(1), 13505 (Jun 2021)","journal-title":"Scientific Reports"},{"issue":"1","key":"16_CR30","doi-asserted-by":"publisher","first-page":"15102","DOI":"10.1038\/s41598-022-19112-9","volume":"12","author":"SC Wetstein","year":"2022","unstructured":"Wetstein, S.C., de\u00a0Jong, V.M.T., Stathonikos, N., Opdam, M., Dackus, G.M.H.E., Pluim, J.P.W., van Diest, P.J., Veta, M.: Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images. Scientific Reports 12(1), 15102 (Sep 2022)","journal-title":"Scientific Reports"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor Fusion Network for Multimodal Sentiment Analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 1103\u20131114. Association for Computational Linguistics (Sep 2017)","DOI":"10.18653\/v1\/D17-1115"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, R., Zhou, H., Chen, B.Y., Shen, L., Zhang, Y., He, L.: Attentive Deep Canonical Correlation Analysis for Diagnosing Alzheimer\u2019s Disease Using Multimodal Imaging Genetics. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. pp. 681\u2013691. Springer Nature Switzerland (2023)","DOI":"10.1007\/978-3-031-43895-0_64"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72384-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:14:36Z","timestamp":1727867676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72384-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723834","9783031723841"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72384-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Elizabeth Cohen-Jonathan Moyal served as a member of expert board for Novocure, received lecture fees from Accuray and Novocure, travel fees from Novocure and research grants from Astra Zeneca, Novocure, Bayer and Incyte. She also received research grants from ARC foundation. All other authors have no competing interests to declare.","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}