{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:45:12Z","timestamp":1774953912305,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"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":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10044-026-01665-4","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:46:39Z","timestamp":1774950399000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-scale hierarchical vision transformer for brain tumor segmentation"],"prefix":"10.1007","volume":"29","author":[{"given":"K.","family":"Indrakumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Ravikumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"issue":"1","key":"1665_CR1","doi-asserted-by":"publisher","first-page":"38275","DOI":"10.1038\/s41598-025-23100-0","volume":"15","author":"C Sankari","year":"2025","unstructured":"Sankari C, Jamuna V, Kavitha AR (2025) Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging. Sci Rep 15(1):38275","journal-title":"Sci Rep"},{"issue":"1s","key":"1665_CR2","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.12732\/ijam.v38i1s.743","volume":"38","author":"S Nandini","year":"2025","unstructured":"Nandini S (2025) DiffuseMed: diffusion-guided multimodal transformer for robust and calibrated medical image segmentation. Int J Appl Math 38(1s):1260\u20131283","journal-title":"Int J Appl Math"},{"key":"1665_CR3","doi-asserted-by":"publisher","first-page":"1682603","DOI":"10.3389\/fnins.2025.1682603","volume":"19","author":"W Chen","year":"2025","unstructured":"Chen W, Cai C, Tan X, Lv R, Zhang J, Du G (2025) MAUNet: a mixed attention U-Net with spatial multi-dimensional convolution and contextual feature calibration for 3D brain tumor segmentation in multimodal MRI. Front NeuroSci 19:1682603","journal-title":"Front NeuroSci"},{"key":"1665_CR4","doi-asserted-by":"publisher","unstructured":"Khushubu KG, Al Masum A, Rahman MH, Hasan SMS, Bhuiyan MIH, Mahmud MR, Appaji A (2025) TransUNetB: an advanced transformer\u2013U-Net framework for efficient and explainable brain tumor segmentation. Inf Med Unlocked 101706. https:\/\/doi.org\/10.1016\/j.imu.2025.101706","DOI":"10.1016\/j.imu.2025.101706"},{"issue":"5","key":"1665_CR5","doi-asserted-by":"publisher","first-page":"e70173","DOI":"10.1002\/ima.70173","volume":"35","author":"J Zhu","year":"2025","unstructured":"Zhu J, Liao Y, Chen Y, Gao F, Li R, Yang G, Chen J (2025) Multimodal MRI-based glioma segmentation and MGMT promoter methylation status prediction using multitask learning architecture. Int J Imaging Syst Technol 35(5):e70173","journal-title":"Int J Imaging Syst Technol"},{"issue":"2","key":"1665_CR6","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s42044-024-00220-w","volume":"8","author":"MAL Khaniki","year":"2025","unstructured":"Khaniki MAL, Mirzaeibonehkhater M, Manthouri M, Hasani E (2025) Vision transformer with feature calibration and selective cross-attention for brain tumor classification. Iran J Comput Sci 8(2):335\u2013347","journal-title":"Iran J Comput Sci"},{"key":"1665_CR7","doi-asserted-by":"publisher","first-page":"111925","DOI":"10.1016\/j.engappai.2025.111925","volume":"160","author":"L Zhang","year":"2025","unstructured":"Zhang L, Li Y, Liang Y, Liu T, Zhang W, Sun J (2025) A lightweight segmentation model based on dilated multi-scale residual attention U-Net for brain tumor segmentation. Eng Appl Artif Intell 160:111925","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"1665_CR8","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/bioengineering12020140","volume":"12","author":"Y Lyu","year":"2025","unstructured":"Lyu Y, Tian X (2025) MWG-UNet++: Hybrid transformer U-Net model for brain tumor segmentation in MRI scans. Bioengineering 12(2):140","journal-title":"Bioengineering"},{"key":"1665_CR9","doi-asserted-by":"publisher","unstructured":"Kulkarni SV, Poornapushpakala S (2025) Multi-scale based network and adaptive EfficientNetB7 with ASPP: analysis of novel brain tumor segmentation and classification. Curr Med Imaging 21(1):E15734056419990. https:\/\/doi.org\/10.2174\/0115734056419990250904093436","DOI":"10.2174\/0115734056419990250904093436"},{"issue":"6","key":"1665_CR10","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s42979-025-04168-x","volume":"6","author":"K Pani","year":"2025","unstructured":"Pani K, Chawla I (2025) Fuzzy Inception U-Net: bridging uncertainties in MRI for multi-modal brain tumor segmentation. SN Comput Sci 6(6):621","journal-title":"SN Comput Sci"},{"key":"1665_CR11","doi-asserted-by":"publisher","unstructured":"Echine K, Darouichi A (2025) 3D AGRes-UNet: an advanced deep learning model for brain tumor segmentation from multimodal MRI. Multimedia Tools Appl 84(34):42135-42154. https:\/\/doi.org\/10.1007\/s11042-025-20828-9","DOI":"10.1007\/s11042-025-20828-9"},{"key":"1665_CR12","doi-asserted-by":"publisher","first-page":"109259","DOI":"10.1016\/j.bspc.2025.109259","volume":"113","author":"Y Li","year":"2026","unstructured":"Li Y, Qin J, Qin G, Zhang F (2026) MAFA-TransUNet: Multi-scale attention and feature aggregation with transformer U-Net for liver tumor segmentation. Biomed Signal Process Control 113:109259","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"1665_CR13","doi-asserted-by":"publisher","first-page":"34393","DOI":"10.1038\/s41598-025-17300-x","volume":"15","author":"S Qamar","year":"2025","unstructured":"Qamar S, Qadri SF, Alroobaea R, Alshmrani GM, Fazil M, Jiang R (2025) ScaleFusionNet: Transformer-guided multi-scale feature fusion for skin lesion segmentation. Sci Rep 15(1):34393","journal-title":"Sci Rep"},{"key":"1665_CR14","doi-asserted-by":"publisher","first-page":"1569083","DOI":"10.3389\/fonc.2025.1569083","volume":"15","author":"S Zheng","year":"2025","unstructured":"Zheng S, Sagar AS, Chen Y, Yu Z, Ying S, Zeng Y (2025) DynTransNet: dynamic transformer network with multi-scale attention for liver cancer segmentation. Front Oncol 15:1569083","journal-title":"Front Oncol"},{"key":"1665_CR15","doi-asserted-by":"publisher","unstructured":"Lin Z, Huang G, Chen Z, Li Y, Yuan X, Pun CM, Cheng L (2025) Multi-scale frequency guided image analyses for mixed-modality medical image segmentation. IEEE Trans Instrum Meas, 74. https:\/\/doi.org\/10.1109\/TIM.2025.3562983","DOI":"10.1109\/TIM.2025.3562983"},{"key":"1665_CR16","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.patrec.2025.01.024","volume":"189","author":"J Li","year":"2025","unstructured":"Li J, Cheng Q, Wu C (2025) GViT-RSNet: a retinal vessel segmentation network using graph convolutional attention and multi-scale vision transformer. Pattern Recognit Lett 189:182\u2013187","journal-title":"Pattern Recognit Lett"},{"key":"1665_CR17","doi-asserted-by":"publisher","first-page":"108842","DOI":"10.1016\/j.bspc.2025.108842","volume":"112","author":"P Qiu","year":"2026","unstructured":"Qiu P, Yang J, Kumar S, Ghosh SS, Sotiras A (2026) AgileFormer: spatially agile and scalable transformer for medical image segmentation. Biomed Signal Process Control 112:108842","journal-title":"Biomed Signal Process Control"},{"key":"1665_CR18","doi-asserted-by":"publisher","unstructured":"Li X, Li L, Xing X, Liao H, Wang W, Dong Q, Yuan C (2025) MSDUNet: a model based on feature multi-scale and dual-input dynamic enhancement for skin lesion segmentation. IEEE Trans Med Imaging, 44(7):2819\u20132830. https:\/\/doi.org\/10.1109\/TMI.2025.3549011","DOI":"10.1109\/TMI.2025.3549011"},{"key":"1665_CR19","doi-asserted-by":"publisher","unstructured":"Wang X, Wang Y, Xu Y, Zhang Y, Zhang L (2025) MMTU-Net: enhancing medical image semantic segmentation with multi-level multi-scale fusion and transformer. Visual Comput 41:8975\u20139003. https:\/\/doi.org\/10.1007\/s00371-025-03909-6","DOI":"10.1007\/s00371-025-03909-6"},{"issue":"1","key":"1665_CR20","doi-asserted-by":"publisher","first-page":"11657","DOI":"10.1038\/s41598-025-93616-y","volume":"15","author":"F Luo","year":"2025","unstructured":"Luo F, Wu D, Pino LR, Ding W (2025) A novel multimodel medical image fusion framework with edge enhancement and cross-scale transformer. Sci Rep 15(1):11657","journal-title":"Sci Rep"},{"key":"1665_CR21","doi-asserted-by":"publisher","unstructured":"Zhou H, Xu L, Liu C, Li G (2025) UDNDSNet: a unified deterministic and non-deterministic segmentation network for multi-scene medical image analysis. Knowl Based Syst 331:114641. https:\/\/doi.org\/10.1016\/j.knosys.2025.114641","DOI":"10.1016\/j.knosys.2025.114641"},{"key":"1665_CR22","doi-asserted-by":"publisher","unstructured":"Mahmoudi H, Ramadan H, Riffi J, Tairi H (2025) 3D-WDA-PMorph: efficient 3D MRI\/TRUS prostate registration using transformer\u2013CNN network and wavelet-based attention. J Imaging Inf Med. https:\/\/doi.org\/10.1007\/s10278-025-01615-2","DOI":"10.1007\/s10278-025-01615-2"},{"key":"1665_CR23","doi-asserted-by":"publisher","first-page":"1557508","DOI":"10.3389\/frai.2025.1557508","volume":"8","author":"P Li","year":"2025","unstructured":"Li P, Ding J, Lim CS (2025) VMDU-Net: a dual encoder multi-scale fusion network for polyp segmentation with vision Mamba and cross-shape transformer integration. Front Artif Intell 8:1557508","journal-title":"Front Artif Intell"},{"key":"1665_CR24","doi-asserted-by":"publisher","first-page":"107475","DOI":"10.1016\/j.bspc.2024.107475","volume":"103","author":"X Guo","year":"2025","unstructured":"Guo X, Sun K, Zheng Y (2025) Multi-scale consistency adversarial learning for semi-supervised 3D medical image segmentation. Biomed Signal Process Control 103:107475","journal-title":"Biomed Signal Process Control"},{"key":"1665_CR25","doi-asserted-by":"publisher","first-page":"1557449","DOI":"10.3389\/fmed.2025.1557449","volume":"12","author":"J Sun","year":"2025","unstructured":"Sun J (2025) MedFusion-TransNet: Multi-modal fusion via transformer for enhanced medical image segmentation. Front Med 12:1557449","journal-title":"Front Med"},{"key":"1665_CR26","doi-asserted-by":"crossref","unstructured":"Liu J, Gao S, Li Y, Liu L, Gao X, Xing Z, Zhuang X (2025) Multi-modal MRI translation via evidential regression and distribution calibration. In: Proceedings MICCAI, p 363\u2013373","DOI":"10.1007\/978-3-032-04984-1_35"},{"key":"1665_CR27","doi-asserted-by":"publisher","first-page":"1577198","DOI":"10.3389\/fonc.2025.1577198","volume":"15","author":"R Wang","year":"2025","unstructured":"Wang R, Lyu C, Yu L (2025) A transformation uncertainty and multi-scale contrastive learning-based semi-supervised segmentation method for oral cavity-derived cancer. Front Oncol 15:1577198","journal-title":"Front Oncol"},{"key":"1665_CR28","doi-asserted-by":"publisher","unstructured":"Mur-Labadia L, Martinez-Cantin R, Guerrero JJ (2025) Uncertainty estimation in instance segmentation of affordances via Bayesian visual transformers. SSRN. https:\/\/doi.org\/10.2139\/ssrn.5313359","DOI":"10.2139\/ssrn.5313359"},{"issue":"4","key":"1665_CR29","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.3390\/app15042222","volume":"15","author":"AS Sagar","year":"2025","unstructured":"Sagar AS, Islam MZ, Tanveer J, Kim HS (2025) Uncertainty-aware adaptive multiscale U-Net for low-contrast cardiac image segmentation. Appl Sci 15(4):2222","journal-title":"Appl Sci"},{"key":"1665_CR30","doi-asserted-by":"publisher","unstructured":"Guo Y, He L (2025) Dual-channel adaptive deep network with uncertainty estimation for colorectal polyp segmentation. Acad J Comput Inform Sci 8(7):17\u201326 https:\/\/doi.org\/10.25236\/AJCIS.2025.080703","DOI":"10.25236\/AJCIS.2025.080703"},{"key":"1665_CR31","doi-asserted-by":"publisher","unstructured":"Wu X, Gou G (2025) Uncertainty bidirectional guidance of multi-task Mamba network for medical image classification and segmentation. SIViP 19(1). https:\/\/doi.org\/10.1007\/s11760-024-03633-z","DOI":"10.1007\/s11760-024-03633-z"},{"key":"1665_CR32","doi-asserted-by":"publisher","first-page":"1589707","DOI":"10.3389\/fmed.2025.1589707","volume":"12","author":"M Arshad","year":"2025","unstructured":"Arshad M, Wang C, Sima MWU, Shaikh JA, Alkhalaf S, Alturise F (2025) RaNet: A residual attention network for accurate prostate segmentation in T2-weighted MRI. Front Med 12:1589707","journal-title":"Front Med"},{"issue":"10","key":"1665_CR33","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.3390\/bioengineering12101051","volume":"12","author":"Y Yang","year":"2025","unstructured":"Yang Y, Wen J, Wu T, Dong J, Xia Y, Zhang Y (2025) LG-UNet based segmentation and survival prediction of nasopharyngeal carcinoma using multimodal MRI imaging. Bioengineering 12(10):1051","journal-title":"Bioengineering"},{"key":"1665_CR34","doi-asserted-by":"publisher","first-page":"107268","DOI":"10.1016\/j.bspc.2024.107268","volume":"102","author":"E Rajasekar","year":"2025","unstructured":"Rajasekar E, Chandra H, Pears N, Vairavasundaram S, Kotecha K (2025) Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning. Biomed Signal Process Control 102:107268","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"1665_CR35","doi-asserted-by":"publisher","first-page":"31138","DOI":"10.1038\/s41598-025-13647-3","volume":"15","author":"S Anand","year":"2025","unstructured":"Anand S, Sharma A, Natarajan B, Slathia AS, Rathi A, Behara KP, Elakkiya R (2025) CHASHNIt for enhancing skin disease classification using GAN augmented hybrid model with LIME and SHAP based XAI heatmaps. Sci Rep 15(1):31138","journal-title":"Sci Rep"},{"issue":"1","key":"1665_CR36","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11042-021-10627-3","volume":"81","author":"R Elakkiya","year":"2022","unstructured":"Elakkiya R, Teja KS, Jegatha Deborah L, Bisogni C, Medaglia C (2022) Imaging based cervical cancer diagnostics using small object detection-generative adversarial networks. Multimed Tools Appl 81(1):191\u2013207","journal-title":"Multimed Tools Appl"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01665-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01665-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01665-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:46:41Z","timestamp":1774950401000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01665-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1665"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01665-4","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"3 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve experiments on human participants or animals that required approval from an ethics committee.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was not applicable as no identifiable human data were used in this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This research did not involve animal experiments.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Animal welfare"}}],"article-number":"80"}}