{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:07:23Z","timestamp":1780603643385,"version":"3.54.1"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92259202"],"award-info":[{"award-number":["92259202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476246"],"award-info":[{"award-number":["62476246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d Research and Development Program of Zhejiang","award":["2025C02132"],"award-info":[{"award-number":["2025C02132"]}]},{"DOI":"10.13039\/501100012166","name":"Guangzhou City\u2019s Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024B01J1301"],"award-info":[{"award-number":["2024B01J1301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1109\/tmi.2026.3668773","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T20:49:25Z","timestamp":1772225365000},"page":"3124-3136","source":"Crossref","is-referenced-by-count":1,"title":["Decouple, Reorganize, and Fuse: A Multimodal Framework for Cancer Survival Prediction"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3371-1482","authenticated-orcid":false,"given":"Huayi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7832-2518","authenticated-orcid":false,"given":"Haochao","family":"Ying","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transvascular Implantation Devices, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4503-4098","authenticated-orcid":false,"given":"Yuyang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2848-6079","authenticated-orcid":false,"given":"Qibo","family":"Qiu","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Research &#x0026; Innovation Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, and Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6565-2884","authenticated-orcid":false,"given":"Danny Z.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5888-2929","authenticated-orcid":false,"given":"Ying","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, and Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3230-6392","authenticated-orcid":false,"given":"Jian","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transvascular Implantation Devices, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2290120"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1037\/0022-006X.57.4.536"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2021.645232"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/9781119627876"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtho.2021.04.004"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/sj.bjc.6601118"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.pbiomolbio.2022.07.004"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.07.004"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/EHB52898.2021.9657722"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00408-3"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-92799-4"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-58798-z"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01903-9"},{"key":"ref14","article-title":"Multimodal data integration for precision oncology: Challenges and future directions","author":"Zhou","year":"2024","journal-title":"arXiv:2406.19611"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109972"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102536"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3059956"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87240-3_64"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_69"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3063150"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3217449"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3147546"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-78191-0_25"},{"key":"ref24","article-title":"Completed feature disentanglement learning for multimodal MRIs analysis","author":"Liu","year":"2024","journal-title":"arXiv:2407.04916"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3455931"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72946-1_3"},{"key":"ref27","article-title":"Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction","author":"Zhang","year":"2024","journal-title":"arXiv:2401.01646"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"ref30","article-title":"GShard: Scaling giant models with conditional computation and automatic sharding","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lepikhin"},{"issue":"120","key":"ref31","first-page":"1","article-title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","volume":"23","author":"Fedus","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i8.32878"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547754"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3095476"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01964"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01100"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72083-3_30"},{"key":"ref39","first-page":"2136","article-title":"TransMIL: Transformer based correlated multiple instance learning for whole slide image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shao"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01942"},{"key":"ref41","first-page":"971","article-title":"Self-normalizing neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Klambauer"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i16.17664"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2024.051556"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s00018-008-8281-1"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1186\/s12943-024-01980-6"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref48","first-page":"269","article-title":"Tutel: Adaptive mixture-of-experts at scale","volume-title":"Proc. Mach. Learn. Syst.","volume":"5","author":"Hwang","year":"2023"},{"key":"ref49","first-page":"8583","article-title":"Scaling vision with sparse mixture of experts","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Riquelme"},{"key":"ref50","article-title":"EvoMoE: An evolutional mixture-of-experts training framework via dense-to-sparse gate","author":"Nie","year":"2021","journal-title":"arXiv:2112.14397"},{"key":"ref51","article-title":"Dense training, sparse inference: Rethinking training of mixture-of-experts language models","author":"Pan","year":"2024","journal-title":"arXiv:2404.05567"},{"key":"ref52","volume-title":"Histopathologic Technique and Practical Histochemistry","author":"Lillie","year":"1954"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1982.03320430047030"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.2307\/2281868"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref56","first-page":"2127","article-title":"Attention-based deep multiple instance learning","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","volume":"80","author":"Ilse"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/s0076-6879(04)86001-4"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02643-7"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2019.08.061"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.cllc.2025.04.009"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1309933111"},{"key":"ref62","first-page":"857","article-title":"Stochastic neighbor embedding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"15","author":"Hinton"},{"key":"ref63","first-page":"3519","article-title":"Similarity of neural network representations revisited","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kornblith"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11550367\/11417210.pdf?arnumber=11417210","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:56:16Z","timestamp":1780602976000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11417210\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":63,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2026.3668773","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]}}}