{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:52:18Z","timestamp":1791132738713,"version":"4.1.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372318"],"award-info":[{"award-number":["62372318"]}],"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":["62102278"],"award-info":[{"award-number":["62102278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s11517-026-03643-8","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T07:38:09Z","timestamp":1785569889000},"page":"3371-3397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal prediction model for brain hematoma expansion based on deep neural network with transformer-driven collaborative attention fusion"],"prefix":"10.1007","volume":"64","author":[{"given":"Nanxu","family":"Tao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9757-4323","authenticated-orcid":false,"given":"Qiming","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"issue":"10","key":"3643_CR1","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/S1474-4422(24)00369-7","volume":"23","author":"VL Feigin","year":"2024","unstructured":"Feigin VL, Abate MD, Abate YH, Abd ElHafeez S, Abd-Allah F, Abdelalim A, Abdelkader A, Abdelmasseh M, Abd-Elsalam S, Abdi P et al (2024) Global, regional, and national burden of stroke and its risk factors, 1990\u20132021: a systematic analysis for the global burden of disease study 2021. The Lancet Neurology 23(10):973\u20131003","journal-title":"The Lancet Neurology"},{"issue":"4","key":"3643_CR2","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1002\/ana.25563","volume":"86","author":"A Morotti","year":"2019","unstructured":"Morotti A, Boulouis G, Dowlatshahi D, Li Q, Barras CD, Delcourt C, Yu Z, Zheng J, Zhou Z, Aviv RI et al (2019) Standards for detecting, interpreting, and reporting noncontrast computed tomographic markers of intracerebral hemorrhage expansion. Ann Neurol 86(4):480\u2013492","journal-title":"Ann Neurol"},{"issue":"1","key":"3643_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1038\/s41746-024-01007-w","volume":"7","author":"AT Tran","year":"2024","unstructured":"Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A et al (2024) Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digital Medicine 7(1):26","journal-title":"NPJ Digital Medicine"},{"key":"3643_CR4","doi-asserted-by":"publisher","first-page":"702","DOI":"10.3389\/fneur.2020.00702","volume":"11","author":"Z Li","year":"2020","unstructured":"Li Z, You M, Long C, Bi R, Xu H, He Q, Hu B (2020) Hematoma expansion in intracerebral hemorrhage: an update on prediction and treatment. Front Neurol 11:702","journal-title":"Front Neurol"},{"issue":"10","key":"3643_CR5","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11547-022-01530-4","volume":"127","author":"S Matsoukas","year":"2022","unstructured":"Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J et al (2022) Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: A systematic review and pooled analysis. Radiol Med (Torino) 127(10):1106\u20131123","journal-title":"Radiol Med (Torino)"},{"issue":"1","key":"3643_CR6","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-024-01166-w","volume":"7","author":"LL Guo","year":"2024","unstructured":"Guo LL, Fries J, Steinberg E, Fleming SL, Morse K, Aftandilian C, Posada J, Shah N, Sung L (2024) A multi-center study on the adaptability of a shared foundation model for electronic health records. NPJ digital medicine 7(1):171","journal-title":"NPJ digital medicine"},{"issue":"12","key":"3643_CR7","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.3390\/bioengineering11121212","volume":"11","author":"SB Lee","year":"2024","unstructured":"Lee SB, Hong Y, Cho YJ, Jeong D, Lee J, Choi JW, Hwang JY, Lee S, Choi YH, Cheon J-E (2024) Enhancing radiomics reproducibility: Deep learning-based harmonization of abdominal computed tomography (ct) images. Bioengineering 11(12):1212","journal-title":"Bioengineering"},{"issue":"8","key":"3643_CR8","doi-asserted-by":"publisher","first-page":"5791","DOI":"10.1007\/s00521-022-06960-9","volume":"34","author":"MA Abdou","year":"2022","unstructured":"Abdou MA (2022) Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Comput Appl 34(8):5791\u20135812","journal-title":"Neural Comput Appl"},{"key":"3643_CR9","doi-asserted-by":"crossref","unstructured":"Massad N, Zhou L, Manolovitz B, Asdaghi N, Gardener H, Ying H, Gutierrez CM, Jameson A, Rose D, Kottapally M et al (2025) Association of the ich score with withdrawal of life-sustaining treatment over a 10-year period. Annals of Clinical and Translational Neurology","DOI":"10.1101\/2025.01.30.25321442"},{"issue":"2","key":"3643_CR10","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1161\/STROKEAHA.114.006910","volume":"46","author":"X Wang","year":"2015","unstructured":"Wang X, Arima H, Al-Shahi Salman R, Woodward M, Heeley E, Stapf C, Lavados PM, Robinson T, Huang Y, Wang J et al (2015) Clinical prediction algorithm (brain) to determine risk of hematoma growth in acute intracerebral hemorrhage. Stroke 46(2):376\u2013381","journal-title":"Stroke"},{"issue":"6","key":"3643_CR11","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.3390\/jcm11061596","volume":"11","author":"AH Katsanos","year":"2022","unstructured":"Katsanos AH, Gupta H, Morotti A, Beshara S, Patil T, Al-Zahrani S, Tsivgoulis G, Dowlatshahi D, Goldstein JN, Charidimou A et al (2022) Increased prognostic yield by combined assessment of non-contrast computed tomography markers of antithrombotic-related spontaneous intracerebral hemorrhage expansion. J Clin Med 11(6):1596","journal-title":"J Clin Med"},{"issue":"12","key":"3643_CR12","doi-asserted-by":"publisher","first-page":"9131","DOI":"10.1007\/s00330-021-08061-y","volume":"31","author":"Q Li","year":"2021","unstructured":"Li Q, Dong F, Wang Q, Xu F, Zhang M (2021) A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of ct features. Eur Radiol 31(12):9131\u20139138","journal-title":"Eur Radiol"},{"issue":"11","key":"3643_CR13","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.1161\/STROKEAHA.117.017985","volume":"48","author":"Q Li","year":"2017","unstructured":"Li Q, Liu Q-J, Yang W-S, Wang X-C, Zhao L-B, Xiong X, Li R, Cao D, Zhu D, Wei X et al (2017) Island sign: an imaging predictor for early hematoma expansion and poor outcome in patients with intracerebral hemorrhage. Stroke 48(11):3019\u20133025","journal-title":"Stroke"},{"issue":"2","key":"3643_CR14","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3348\/kjr.2023.0591","volume":"25","author":"L Song","year":"2024","unstructured":"Song L, Qiu X, Zhang C, Zhou H, Guo W, Ye Y, Wang R, Xiong H, Zhang J, Tang D et al (2024) Combining non-contrast ct signs with onset-to-imaging time to predict the evolution of intracerebral hemorrhage. Korean J Radiol 25(2):166","journal-title":"Korean J Radiol"},{"issue":"1","key":"3643_CR15","doi-asserted-by":"publisher","first-page":"12452","DOI":"10.1038\/s41598-022-15400-6","volume":"12","author":"S Tanioka","year":"2022","unstructured":"Tanioka S, Yago T, Tanaka K, Ishida F, Kishimoto T, Tsuda K, Ikezawa M, Araki T, Miura Y, Suzuki H (2022) Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage. Sci Rep 12(1):12452","journal-title":"Sci Rep"},{"issue":"1","key":"3643_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.acra.2024.07.039","volume":"32","author":"N Li","year":"2025","unstructured":"Li N, Ding S, Liu Z, Ye W, Liu P, Jing J, Jiang Y, Zhao X, Liu T (2025) A deep learning-based framework for predicting intracerebral hematoma expansion using head non-contrast ct scan. Acad Radiol 32(1):347\u2013358","journal-title":"Acad Radiol"},{"issue":"1","key":"3643_CR17","doi-asserted-by":"publisher","first-page":"28920","DOI":"10.1038\/s41598-024-79090-y","volume":"14","author":"J Fan","year":"2024","unstructured":"Fan J, Fan X, Song C, Wang X, Feng B, Li L, Lu G (2024) Dual-task vision transformer for rapid and accurate intracerebral hemorrhage ct image classification. Sci Rep 14(1):28920","journal-title":"Sci Rep"},{"key":"3643_CR18","doi-asserted-by":"crossref","unstructured":"Xiong Z, Zhao K, Ji L, Shu X, Long D, Chen S, Yang F (2024) Multi-modality 3d cnn transformer for assisting clinical decision in intracerebral hemorrhage. In: International conference on medical image computing and computer-assisted intervention, pp 522\u2013531. Springer","DOI":"10.1007\/978-3-031-72086-4_49"},{"issue":"3","key":"3643_CR19","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/bioengineering10030393","volume":"10","author":"T Zhao","year":"2023","unstructured":"Zhao T, Fu C, Tian Y et al (2023) Gsn-hvnet: A lightweight, multi-task deep learning framework for nuclei segmentation and classification. Bioengineering 10(3):393","journal-title":"Bioengineering"},{"issue":"8","key":"3643_CR20","doi-asserted-by":"publisher","first-page":"957","DOI":"10.3390\/bioengineering10080957","volume":"10","author":"T Zhao","year":"2023","unstructured":"Zhao T, Fu C, Tie M et al (2023) Rgsb-unet: Hybrid deep learning framework for tumour segmentation in digital pathology images. Bioengineering 10(8):957","journal-title":"Bioengineering"},{"issue":"1","key":"3643_CR21","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3390\/bioengineering11010016","volume":"11","author":"T Zhao","year":"2023","unstructured":"Zhao T, Fu C, Song W et al (2023) Rggc-unet: Accurate deep learning framework for signet ring cell semantic segmentation in pathological images. Bioengineering 11(1):16","journal-title":"Bioengineering"},{"key":"3643_CR22","doi-asserted-by":"crossref","unstructured":"Guo D, Terzopoulos D (2021) A transformer-based network for anisotropic 3d medical image segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp 8857\u20138861. IEEE","DOI":"10.1109\/ICPR48806.2021.9411990"},{"key":"3643_CR23","doi-asserted-by":"crossref","unstructured":"Lu J, Ding H, Zhang S et al (2025) M-net: Mri brain tumor sequential segmentation network via mesh-cast. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 20116\u201320125","DOI":"10.1109\/ICCV51701.2025.01871"},{"issue":"4","key":"3643_CR24","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s00234-024-03298-y","volume":"66","author":"H Lee","year":"2024","unstructured":"Lee H, Lee J, Jang J, Hwang I, Choi KS, Park JH, Chung JW, Choi SH (2024) Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head ct. Neuroradiology 66(4):577\u2013587","journal-title":"Neuroradiology"},{"issue":"1","key":"3643_CR25","doi-asserted-by":"publisher","first-page":"16465","DOI":"10.1038\/s41598-024-67365-3","volume":"14","author":"S Tanioka","year":"2024","unstructured":"Tanioka S, Aydin OU, Hilbert A, Ishida F, Tsuda K, Araki T, Nakatsuka Y, Yago T, Kishimoto T, Ikezawa M et al (2024) Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network. Sci Rep 14(1):16465","journal-title":"Sci Rep"},{"key":"3643_CR26","doi-asserted-by":"crossref","unstructured":"Liu X, Qiu H, Li M, Yu Z, Yang Y, Yan Y (2024) Application of multimodal fusion deep learning model in disease recognition. In: 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE), pp 1246\u20131250. IEEE","DOI":"10.1109\/ICSECE61636.2024.10729504"},{"issue":"6","key":"3643_CR27","doi-asserted-by":"publisher","first-page":"658","DOI":"10.3174\/ajnr.A7871","volume":"44","author":"C Ducroux","year":"2023","unstructured":"Ducroux C, Nehme A, Rioux B, Panzini M-A, Fahed R, Gioia L, L\u00e9tourneau-Guillon L (2023) Ncct markers of intracerebral hemorrhage expansion using revised criteria: an external validation of their predictive accuracy. Am J Neuroradiol 44(6):658\u2013664","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"3643_CR28","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1093\/bib\/bbab569","volume":"23","author":"SR Stahlschmidt","year":"2022","unstructured":"Stahlschmidt SR, Ulfenborg B, Synnergren J (2022) Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform 23(2):569","journal-title":"Brief Bioinform"},{"issue":"49","key":"3643_CR29","doi-asserted-by":"publisher","first-page":"31914","DOI":"10.1097\/MD.0000000000031914","volume":"101","author":"GH Chung","year":"2022","unstructured":"Chung GH, Goo JH, Kwak HS, Hwang SB (2022) The comprehensive comparison of imaging sign from ct angiography and noncontrast ct for predicting intracranial hemorrhage expansion: A comparative study. Medicine 101(49):31914","journal-title":"Medicine"},{"key":"3643_CR30","doi-asserted-by":"crossref","unstructured":"Isensee F, Petersen J, Klein A et al (2018) nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv:1809.10486","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"3643_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127404","volume":"279","author":"B Liu","year":"2025","unstructured":"Liu B, Song W, Zheng M et al (2025) Semantically enhanced selective image encryption scheme with parallel computing. Expert Syst Appl 279:127404","journal-title":"Expert Syst Appl"},{"key":"3643_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103686","volume":"80","author":"W Song","year":"2024","unstructured":"Song W, Fu C, Zheng Y et al (2024) Batch image encryption using cross image permutation and diffusion. Journal of Information Security and Applications 80:103686","journal-title":"Journal of Information Security and Applications"},{"issue":"11","key":"3643_CR33","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1161\/STROKEAHA.115.009893","volume":"46","author":"TJ Huynh","year":"2015","unstructured":"Huynh TJ, Aviv RI, Dowlatshahi D, Gladstone DJ, Laupacis A, Kiss A, Hill MD, Molina CA, Rodriguez-Luna D, Dzialowski I et al (2015) Validation of the 9-point and 24-point hematoma expansion prediction scores and derivation of the predict a\/b scores. Stroke 46(11):3105\u20133110","journal-title":"Stroke"},{"key":"3643_CR34","first-page":"18932","volume":"34","author":"Y Gorishniy","year":"2021","unstructured":"Gorishniy Y, Rubachev I, Khrulkov V, Babenko A (2021) Revisiting deep learning models for tabular data. Adv Neural Inf Process Syst 34:18932\u201318943","journal-title":"Adv Neural Inf Process Syst"},{"key":"3643_CR35","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929"},{"issue":"17","key":"3643_CR36","doi-asserted-by":"publisher","first-page":"26783","DOI":"10.1002\/hbm.26783","volume":"45","author":"Y Bi","year":"2024","unstructured":"Bi Y, Abrol A, Fu Z et al (2024) A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Hum Brain Mapp 45(17):26783","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"3643_CR37","doi-asserted-by":"publisher","first-page":"23979","DOI":"10.1038\/s41598-025-09041-8","volume":"15","author":"G M\u00fcller-Franzes","year":"2025","unstructured":"M\u00fcller-Franzes G, Khader F, Siepmann R et al (2025) Medical slice transformer for improved diagnosis and explainability on 3d medical images with dinov2. Sci Rep 15(1):23979","journal-title":"Sci Rep"},{"key":"3643_CR38","doi-asserted-by":"crossref","unstructured":"Ouyang J, Xu W, Luo X, Yin X, Zhou Y, Chen L, Yin J, Chen Z (2025) Machine learning-based radiomics model for predicting hematoma enlargement in spontaneous intracerebral hemorrhage: A multicenter study. Brain Hemorrhages","DOI":"10.1016\/j.hest.2025.09.001"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03643-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03643-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03643-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:03:08Z","timestamp":1791129788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03643-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,1]]},"references-count":38,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["3643"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03643-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,1]]},"assertion":[{"value":"28 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work presented in this paper.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}