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Quantitative Methods."},{"key":"10.1016\/j.eswa.2025.130566_bib0066","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2298","article-title":"Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications","volume":"10","author":"Teoh","year":"2024","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.eswa.2025.130566_bib0067","article-title":"Graph attention networks","author":"Velickovic","year":"2017","journal-title":"International Conference on Learning Representations"},{"key":"10.1016\/j.eswa.2025.130566_bib0068","series-title":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"1243","article-title":"AM-GCN: Adaptive multi-channel graph convolutional networks","author":"Wang","year":"2020"},{"key":"10.1016\/j.eswa.2025.130566_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102535","article-title":"Adversarial multimodal fusion with attention mechanism for skin lesion classification using clinical and dermoscopic images","volume":"81","author":"Wang","year":"2022","journal-title":"Medical Image Analysis"},{"issue":"19","key":"10.1016\/j.eswa.2025.130566_bib0070","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ae07a1","article-title":"Medformer: Hierarchical medical vision transformer with content-aware dual sparse selection attention","volume":"70","author":"Xia","year":"2025","journal-title":"Physics in Medicine & Biology"},{"key":"10.1016\/j.eswa.2025.130566_bib0071","series-title":"Proceedings of the 41st international conference on machine learning (ICML)","article-title":"Less is more: On the over-globalizing problem in graph transformers","author":"Xing","year":"2024"},{"issue":"1","key":"10.1016\/j.eswa.2025.130566_bib0072","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/s41746-022-00742-2","article-title":"A large language model for electronic health records","volume":"5","author":"Yang","year":"2022","journal-title":"NPJ Digital Medicine"},{"key":"10.1016\/j.eswa.2025.130566_bib0073","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125713","article-title":"A cognitive few-shot learning for medical diagnosis: A case study on cleft lip and palate and parkinson\u2019s disease","volume":"262","author":"Yin","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.130566_bib0074","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"5756","article-title":"SHVIT: Single-head vision transformer with memory efficient macro design","author":"Yun","year":"2024"},{"key":"10.1016\/j.eswa.2025.130566_bib0075","unstructured":"Zhang, Q., Wei, Y., Han, Z., Fu, H., Peng, X., Deng, C., Hu, Q., Xu, C., Wen, J., Hu, D. et al. (2024). Multimodal fusion on low-quality data: A comprehensive survey. arXiv: 2404.18947."},{"key":"10.1016\/j.eswa.2025.130566_bib0076","unstructured":"Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N. et al. (2023). Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv: 2303.00915."},{"issue":"2","key":"10.1016\/j.eswa.2025.130566_bib0077","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.inffus.2005.04.003","article-title":"A new metric based on extended spatial frequency and its application to DWT based fusion algorithms","volume":"8","author":"Zheng","year":"2007","journal-title":"Information Fusion"},{"issue":"1","key":"10.1016\/j.eswa.2025.130566_bib0078","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s40035-023-00378-6","article-title":"Role of dopamine in the pathophysiology of parkinson\u2019s disease","volume":"12","author":"Zhou","year":"2023","journal-title":"Translational Neurodegeneration"},{"issue":"1","key":"10.1016\/j.eswa.2025.130566_bib0079","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s41531-024-00828-6","article-title":"Multimodal neuroimaging-based prediction of parkinson\u2019s disease with mild cognitive impairment using machine learning technique","volume":"10","author":"Zhu","year":"2024","journal-title":"NPJ Parkinson\u2019s Disease"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425041818?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425041818?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T11:05:17Z","timestamp":1771499117000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417425041818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":79,"alternative-id":["S0957417425041818"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130566","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing multimodal medical image classification through cross-graph modal contrastive learning","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130566","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. 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