{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:14:39Z","timestamp":1783523679681,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819224791","type":"print"},{"value":"9789819224807","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2480-7_7","type":"book-chapter","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:21:19Z","timestamp":1783520479000},"page":"95-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Top-K: Representative Evidence Selection for Retrieval-Augmented Missing-Modality Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2859-6233","authenticated-orcid":false,"given":"Zhipeng","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-1345","authenticated-orcid":false,"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3645-9046","authenticated-orcid":false,"given":"Yufei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8607-8209","authenticated-orcid":false,"given":"Zhikang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4830-8621","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1262-8723","authenticated-orcid":false,"given":"Peiling","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8595-0469","authenticated-orcid":false,"given":"Yuyang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Krones, F., et\u00a0al.: Review of multimodal machine learning approaches in healthcare,Artificial Intelligence in Medicine (2025)","DOI":"10.2139\/ssrn.4736389"},{"key":"7_CR2","unstructured":"Schouten, D., et\u00a0al.: Navigating the landscape of multimodal ai in medicine, Medical Image Analysis (2025)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Reas, E.\u00a0T., et\u00a0al.: Improved multimodal prediction of progression from MCI to alzheimer\u2019s disease combining genetics with quantitative brain MRI and cognitive measures, Alzheimer\u2019s & Dementia (2023)","DOI":"10.1002\/alz.13112"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Jasodanand, V.\u00a0H., et\u00a0al.: Ai-driven fusion of multimodal data for alzheimer\u2019s disease, Nature Communications (2025)","DOI":"10.1038\/s41467-025-62590-4"},{"key":"7_CR5","unstructured":"Wu, R., Wang, H., Chen, H.-T., Carneiro, G.: Deep multimodal learning with missing modality: A survey, arXiv preprint arXiv:2409.07825 (2024)"},{"key":"7_CR6","first-page":"2302","volume":"35","author":"M Ma","year":"2021","unstructured":"Ma, M., Ren, J., Zhao, L., Tulyakov, S., Wu, C., Peng, X.: Smil: multimodal learning with severely missing modality. Proc. AAAI Conf. Artif. Intell. 35, 2302\u20132310 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"10","key":"7_CR7","doi-asserted-by":"publisher","first-page":"6839","DOI":"10.1109\/TPAMI.2021.3091214","volume":"44","author":"Y Pan","year":"2022","unstructured":"Pan, Y., Liu, M., Xia, Y., Shen, D.: Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6839\u20136853 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Peng, C., Wang, Q., Song, D., Li, K., Zhou, S.\u00a0K.: Unified multi-modal image synthesis for missing modality imputation, IEEE Transactions on Medical Imaging (2024)","DOI":"10.1109\/TMI.2024.3424785"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: Hemis: Hetero-modal image segmentation, in Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.\u00a0469\u2013477 (2016)","DOI":"10.1007\/978-3-319-46723-8_54"},{"key":"7_CR10","unstructured":"Zhang, C., Han, Z., Cui, Y., Fu, H., Zhou, J.\u00a0T., Hu, Q.: CPM-Nets: cross partial multi-view networks, in Advances in Neural Information Processing Systems (NeurIPS). vol.\u00a032 (2019)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, Y., Ma, C., Avery, J., Hull, L., Carneirom, G.: Multi-modal learning with missing modality via shared-specific feature modelling, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\u00a015878\u201315888 (2023)","DOI":"10.1109\/CVPR52729.2023.01524"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Yao, W., Guan, Q., Rosenthal, S., Yang, Y.: Drfuse: Learning disentangled representations for clinical multi-modal fusion with missing modality and modal inconsistency, in Proceedings of the AAAI Conference on Artificial Intelligence (2024)","DOI":"10.1609\/aaai.v38i15.29578"},{"key":"7_CR13","unstructured":"S.\u00a0Yun, et al.: Generate, then retrieve: Addressing missing modalities in multimodal learning via generative ai and moe, in Workshop on Large Language Models and Generative AI for Health at AAAI 2025 (2025)"},{"key":"7_CR14","unstructured":"Yun, S., et al.: Generate, then retrieve: Addressing missing modalities in multimodal learning via generative ai and moe, in Workshop on Large Language Models and Generative AI for Health at AAAI 2025 (2025)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Zhang, et al.: mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation, in Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.\u00a0107\u2013117 (2022)","DOI":"10.1007\/978-3-031-16443-9_11"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.-P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"120","key":"7_CR17","first-page":"1","volume":"23","author":"W Fedus","year":"2022","unstructured":"Fedus, W., Zoph, B., Shazeer, N.: Switch transformers: scaling to trillion parameter models with simple and efficient sparsity. J. Mach. Learn. Res. 23(120), 1\u201339 (2022)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR18","unstructured":"Jin, P., Zhu, B., Yuan, L., Yan, S.: Moe++: Accelerating mixture-of-experts methods with zero-computation experts. URL arXiv:2410.07348 (2024)"},{"key":"7_CR19","unstructured":"Xin, J., et al.: I2moe: Interpretable multimodal interaction-aware mixture-of-experts, arXiv preprint arXiv:2505.19190 (2025)"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"67850","DOI":"10.52202\/079017-2167","volume":"37","author":"X Han","year":"2024","unstructured":"Han, X., Nguyen, H., Harris, C., Ho, N., Saria, S.: Fusemoe: Mixture-of-experts transformers for fleximodal fusion. Adv. Neural. Inf. Process. Syst. 37, 67850\u201367900 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR21","unstructured":"Z.\u00a0Wu, et al.: Multimodal patient representation learning with missing modalities and labels, in The Twelfth International Conference on Learning Representations (2024)"},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"98782","DOI":"10.52202\/079017-3135","volume":"37","author":"S Yun","year":"2024","unstructured":"Yun, S., et al.: Flex-moe: modeling arbitrary modality combination via the flexible mixture-of-experts. Adv. Neural. Inf. Process. Syst. 37, 98782\u201398805 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Engineering for Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2480-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:21:22Z","timestamp":1783520482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2480-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"ISBN":["9789819224791","9789819224807"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2480-7_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"9 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FLINS-ISKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Knowledge Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iske2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.flins.cc","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}