{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:59:07Z","timestamp":1776448747225,"version":"3.51.2"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00787-7","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T19:57:56Z","timestamp":1742500676000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation"],"prefix":"10.1007","volume":"18","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yucheng","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faizan","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tariq","family":"Mahmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"787_CR1","volume":"149","author":"T Zhou","year":"2024","unstructured":"Zhou, T.: Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning. Pattern Recogn. 149, 110282 (2024)","journal-title":"Pattern Recogn."},{"issue":"11","key":"787_CR2","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1158\/1078-0432.CCR-23-4033","volume":"30","author":"JR Hansford","year":"2024","unstructured":"Hansford, J.R., Das, A., McGee, R.B., Nakano, Y., Brzezinski, J., Scollon, S.R., Rednam, S.P., Schienda, J., Michaeli, O., Kim, S.Y., et al.: Update on cancer predisposition syndromes and surveillance guidelines for childhood brain tumors. Clin. Cancer Res. 30(11), 2342\u20132350 (2024)","journal-title":"Clin. Cancer Res."},{"key":"787_CR3","unstructured":"You, C., Dai, W., Min, Y., Liu, F., Clifton, D., Zhou, S.K., Staib, L., Duncan, J.: Rethinking semi-supervised medical image segmentation: A variance-reduction perspective. Advances in neural information processing systems 36 (2024)"},{"issue":"9","key":"787_CR4","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.1109\/TMI.2022.3161829","volume":"41","author":"C You","year":"2022","unstructured":"You, C., Zhou, Y., Zhao, R., Staib, L., Duncan, J.S.: Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation. IEEE Trans. Med. Imaging 41(9), 2228\u20132237 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"787_CR5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108464","volume":"257","author":"R Agarwal","year":"2024","unstructured":"Agarwal, R., Ghosal, P., Sadhu, A.K., Murmu, N., Nandi, D.: Multi-scale dual-channel feature embedding decoder for biomedical image segmentation. Comput. Methods Programs Biomed. 257, 108464 (2024)","journal-title":"Comput. Methods Programs Biomed."},{"key":"787_CR6","doi-asserted-by":"crossref","unstructured":"You, C., Zhao, R., Staib, L.H., Duncan, J.S.: Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 639\u2013652 (2022). Springer","DOI":"10.1007\/978-3-031-16440-8_61"},{"key":"787_CR7","first-page":"29582","volume":"35","author":"C You","year":"2022","unstructured":"You, C., Zhao, R., Liu, F., Dong, S., Chinchali, S., Topcu, U., Staib, L., Duncan, J.: Class-aware adversarial transformers for medical image segmentation. Adv. Neural. Inf. Process. Syst. 35, 29582\u201329596 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"787_CR8","doi-asserted-by":"crossref","unstructured":"Ghosal, P., Roy, A., Agarwal, R., Purkayastha, K., Sharma, A.L., Kumar, A.: Compound attention embedded dual channel encoder-decoder for ms lesion segmentation from brain mri. Multimedia Tools and Applications, 1\u201333 (2024)","DOI":"10.1007\/s11042-024-20416-3"},{"issue":"1","key":"787_CR9","doi-asserted-by":"crossref","first-page":"448","DOI":"10.56028\/aetr.10.1.448.2024","volume":"10","author":"W Yang","year":"2024","unstructured":"Yang, W.: Brain resonance imaging segmentation method based on deep resunet. Advances in Engineering Technology Research 10(1), 448\u2013448 (2024)","journal-title":"Advances in Engineering Technology Research"},{"key":"787_CR10","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Duncan, J.S.: Implicit anatomical rendering for medical image segmentation with stochastic experts. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 561\u2013571 (2023). Springer","DOI":"10.1007\/978-3-031-43898-1_54"},{"key":"787_CR11","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108434","volume":"124","author":"L Fang","year":"2022","unstructured":"Fang, L., Wang, X.: Brain tumor segmentation based on the dual-path network of multi-modal mri images. Pattern Recogn. 124, 108434 (2022)","journal-title":"Pattern Recogn."},{"key":"787_CR12","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Sekhon, J., Duncan, J.S.: Action++: Improving semi-supervised medical image segmentation with adaptive anatomical contrast. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 194\u2013205 (2023). Springer","DOI":"10.1007\/978-3-031-43901-8_19"},{"issue":"4","key":"787_CR13","first-page":"417","volume":"40","author":"C Chang","year":"2021","unstructured":"Chang, C., Junbo, C., Xinhao, C.: Magnetic resonance image segmentation of brain tumor based on improved u-net method [j]. Journal of South-Central University for Nationalities 40(4), 417\u2013423 (2021)","journal-title":"Journal of South-Central University for Nationalities"},{"issue":"1","key":"787_CR14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., et al.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 87\u2013110 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"787_CR15","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.vrih.2024.04.001","volume":"6","author":"W Lai","year":"2024","unstructured":"Lai, W., Menghan, H.: A review of medical ocular image segmentation. Virtual Reality & Intelligent Hardware 6(3), 181\u2013202 (2024)","journal-title":"Virtual Reality & Intelligent Hardware"},{"issue":"2","key":"787_CR16","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1007\/s11227-021-03901-6","volume":"78","author":"H Jiang","year":"2022","unstructured":"Jiang, H., Diao, Z., Yao, Y.-D.: Deep learning techniques for tumor segmentation: a review. J. Supercomput. 78(2), 1807\u20131851 (2022)","journal-title":"J. Supercomput."},{"key":"787_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhu, Y., Mei, X., Huang, L., Ma, J., He, K., Peng, Z., Cambria, E., Feng, M.: Has multimodal learning delivered universal intelligence in healthcare? a comprehensive survey. arXiv preprint arXiv:2408.12880 (2024)","DOI":"10.1016\/j.inffus.2024.102795"},{"key":"787_CR18","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.neucom.2022.04.051","volume":"493","author":"J Huang","year":"2022","unstructured":"Huang, J., Fang, Y., Wu, Y., Wu, H., Gao, Z., Li, Y., Del Ser, J., Xia, J., Yang, G.: Swin transformer for fast mri. Neurocomputing 493, 281\u2013304 (2022)","journal-title":"Neurocomputing"},{"key":"787_CR19","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Liu, F., Min, Y., Dvornek, N.C., Li, X., Clifton, D.A., Staib, L., Duncan, J.S.: Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","DOI":"10.1109\/TPAMI.2024.3461321"},{"key":"787_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wu, Y., Yang, B., Hu, S., Wu, L., Dhelim, S.: Overview of multi-modal brain tumor mr image segmentation. In: Healthcare, vol. 9, p. 1051 (2021). MDPI","DOI":"10.3390\/healthcare9081051"},{"key":"787_CR21","doi-asserted-by":"crossref","unstructured":"Saqib, M., Zahoor, A., Rahib, A., Shamim, A., Mumtaz, H.: Clinical and translational advances in primary brain tumor therapy with a focus on glioblastoma-a comprehensive review of the literature. World Neurosurgery: X, 100399 (2024)","DOI":"10.1016\/j.wnsx.2024.100399"},{"key":"787_CR22","doi-asserted-by":"crossref","unstructured":"You, C., Yang, J., Chapiro, J., Duncan, J.S.: Unsupervised wasserstein distance guided domain adaptation for 3d multi-domain liver segmentation. In: Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings 3, pp. 155\u2013163 (2020). Springer","DOI":"10.1007\/978-3-030-61166-8_17"},{"key":"787_CR23","doi-asserted-by":"crossref","unstructured":"Chen, C., Dou, Q., Jin, Y., Chen, H., Qin, J., Heng, P.-A.: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22, pp. 447\u2013456 (2019). Springer","DOI":"10.1007\/978-3-030-32248-9_50"},{"key":"787_CR24","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Duncan, J.S.: Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive distillation. In: International Conference on Information Processing in Medical Imaging, pp. 641\u2013653 (2023). Springer","DOI":"10.1007\/978-3-031-34048-2_49"},{"issue":"6","key":"787_CR25","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.37188\/CO.2022-0105","volume":"15","author":"C Zuo","year":"2022","unstructured":"Zuo, C., Chen, Q.: Resolution, super-resolution and spatial bandwidth product expansion-some thoughts from the perspective of computational optical imaging. Chinese Optics 15(6), 1105\u20131166 (2022)","journal-title":"Chinese Optics"},{"key":"787_CR26","doi-asserted-by":"crossref","unstructured":"You, C., Xiang, J., Su, K., Zhang, X., Dong, S., Onofrey, J., Staib, L., Duncan, J.S.: Incremental learning meets transfer learning: Application to multi-site prostate mri segmentation. In: International Workshop on Distributed, Collaborative, and Federated Learning, pp. 3\u201316 (2022). Springer","DOI":"10.1007\/978-3-031-18523-6_1"},{"key":"787_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, Y., Zhu, Z., Cheng, J., Chen, X.: Transsea: Hybrid cnn-transformer with semantic awareness for 3d brain tumor segmentation. IEEE Transactions on Instrumentation and Measurement (2024)","DOI":"10.1109\/TIM.2024.3413130"},{"key":"787_CR28","volume":"153","author":"Z Zhu","year":"2024","unstructured":"Zhu, Z., Wang, Z., Qi, G., Mazur, N., Yang, P., Liu, Y.: Brain tumor segmentation in mri with multi-modality spatial information enhancement and boundary shape correction. Pattern Recogn. 153, 110553 (2024)","journal-title":"Pattern Recogn."},{"issue":"4","key":"787_CR29","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s00530-020-00694-1","volume":"27","author":"C Bhatt","year":"2021","unstructured":"Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K.U., Kumar, A.: The state of the art of deep learning models in medical science and their challenges. Multimedia Syst. 27(4), 599\u2013613 (2021)","journal-title":"Multimedia Syst."},{"issue":"1","key":"787_CR30","first-page":"0286125","volume":"19","author":"X Wu","year":"2024","unstructured":"Wu, X., Yang, X., Li, Z., Liu, L., Xia, Y.: Multimodal brain tumor image segmentation based on densenet. PLoS ONE 19(1), 0286125 (2024)","journal-title":"PLoS ONE"},{"key":"787_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2024.101892","volume":"21","author":"I Aboussaleh","year":"2024","unstructured":"Aboussaleh, I., Riffi, J., el Fazazy, K., Mahraz, A.M., Tairi, H.: 3duv-netr+: A 3d hybrid semantic architecture using transformers for brain tumor segmentation with multimodal mr images. Results in Engineering 21, 101892 (2024)","journal-title":"Results in Engineering"},{"key":"787_CR32","doi-asserted-by":"crossref","unstructured":"Liu, H., Ni, Z., Nie, D., Shen, D., Wang, J., Tang, Z.: Multimodal brain tumor segmentation boosted by monomodal normal brain images. IEEE Transactions on Image Processing (2024)","DOI":"10.1109\/TIP.2024.3359815"},{"key":"787_CR33","volume":"80","author":"Y Peng","year":"2023","unstructured":"Peng, Y., Sun, J.: The multimodal mri brain tumor segmentation based on ad-net. Biomed. Signal Process. Control 80, 104336 (2023)","journal-title":"Biomed. Signal Process. Control"},{"issue":"7","key":"787_CR34","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/JBHI.2023.3271808","volume":"27","author":"H Yang","year":"2023","unstructured":"Yang, H., Zhou, T., Zhou, Y., Zhang, Y., Fu, H.: Flexible fusion network for multi-modal brain tumor segmentation. IEEE J. Biomed. Health Inform. 27(7), 3349\u20133359 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"10","key":"787_CR35","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"787_CR36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103861","volume":"79","author":"R Raza","year":"2023","unstructured":"Raza, R., Bajwa, U.I., Mehmood, Y., Anwar, M.W., Jamal, M.H.: dresu-net: 3d deep residual u-net based brain tumor segmentation from multimodal mri. Biomed. Signal Process. Control 79, 103861 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"787_CR37","volume":"112","author":"Y Al Khalil","year":"2024","unstructured":"Al Khalil, Y., Ayaz, A., Lorenz, C., Weese, J., Pluim, J., Breeuwer, M.: Multi-modal brain tumor segmentation via conditional synthesis with fourier domain adaptation. Comput. Med. Imaging Graph. 112, 102332 (2024)","journal-title":"Comput. Med. Imaging Graph."},{"key":"787_CR38","volume":"122","author":"MJ Hua","year":"2024","unstructured":"Hua, M.J., Wu, J., Zhong, Z.: Multi-scale knowledge transfer vision transformer for 3d vessel shape segmentation. Computers & Graphics 122, 103976 (2024)","journal-title":"Computers & Graphics"},{"key":"787_CR39","unstructured":"Rossow, W.B.: Global weather states from isccp hgg cloud dataset"},{"issue":"1","key":"787_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1\u201313 (2017)","journal-title":"Scientific data"},{"issue":"7","key":"787_CR41","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.3390\/life13071449","volume":"13","author":"AA Asiri","year":"2023","unstructured":"Asiri, A.A., Shaf, A., Ali, T., Aamir, M., Irfan, M., Alqahtani, S., Mehdar, K.M., Halawani, H.T., Alghamdi, A.H., Alshamrani, A., et al.: Brain tumor detection and classification using fine-tuned cnn with resnet50 and u-net model: A study on tcga-lgg and tcia dataset for mri applications. Life 13(7), 1449 (2023)","journal-title":"Life"},{"key":"787_CR42","unstructured":"Cheng, J.: Brain magnetic resonance imaging tumor dataset. Figshare MRI dataset version 5 (2017)"},{"key":"787_CR43","doi-asserted-by":"crossref","DOI":"10.1201\/b18611","volume-title":"Electric, electronic and control engineering","author":"F Shao","year":"2015","unstructured":"Shao, F., Shu, W., Tian, T.: Electric, electronic and control engineering. CRC Press. Google Scholar Google Scholar Cross Ref Cross Ref, Phuker (2015)"},{"key":"787_CR44","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108434","volume":"124","author":"L Fang","year":"2022","unstructured":"Fang, L., Wang, X.: Brain tumor segmentation based on the dual-path network of multi-modal mri images. Pattern Recogn. 124, 108434 (2022)","journal-title":"Pattern Recogn."},{"key":"787_CR45","doi-asserted-by":"crossref","unstructured":"Ghosal, P., Reddy, S., Sai, C., Pandey, V., Chakraborty, J., Nandi, D.: A deep adaptive convolutional network for brain tumor segmentation from multimodal mr images. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pp. 1065\u20131070 (2019). IEEE","DOI":"10.1109\/TENCON.2019.8929402"},{"issue":"6","key":"787_CR46","doi-asserted-by":"crossref","first-page":"3247","DOI":"10.1016\/j.jksuci.2022.03.022","volume":"34","author":"AS Akbar","year":"2022","unstructured":"Akbar, A.S., Fatichah, C., Suciati, N.: Single level unet3d with multipath residual attention block for brain tumor segmentation. Journal of King Saud University-Computer and Information Sciences 34(6), 3247\u20133258 (2022)","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"issue":"10","key":"787_CR47","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.1109\/TMI.2022.3175478","volume":"41","author":"Q Yang","year":"2022","unstructured":"Yang, Q., Guo, X., Chen, Z., Woo, P.Y., Yuan, Y.: D 2-net: Dual disentanglement network for brain tumor segmentation with missing modalities. IEEE Trans. Med. Imaging 41(10), 2953\u20132964 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"787_CR48","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"787_CR49","unstructured":"Vaswani, A.: Attention is all you need. Advances in Neural Information Processing Systems (2017)"},{"key":"787_CR50","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015). Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"787_CR51","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016). Ieee","DOI":"10.1109\/3DV.2016.79"},{"issue":"6","key":"787_CR52","doi-asserted-by":"crossref","first-page":"797","DOI":"10.3390\/brainsci12060797","volume":"12","author":"Y Jiang","year":"2022","unstructured":"Jiang, Y., Zhang, Y., Lin, X., Dong, J., Cheng, T., Liang, J.: Swinbts: A method for 3d multimodal brain tumor segmentation using swin transformer. Brain Sci. 12(6), 797 (2022)","journal-title":"Brain Sci."},{"key":"787_CR53","volume":"113","author":"X Liu","year":"2025","unstructured":"Liu, X., Gao, P., Yu, T., Wang, F., Yuan, R.-Y.: Cswin-unet: Transformer unet with cross-shaped windows for medical image segmentation. Information Fusion 113, 102634 (2025)","journal-title":"Information Fusion"},{"key":"787_CR54","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106633","volume":"96","author":"MR Islam","year":"2024","unstructured":"Islam, M.R., Qaraqe, M., Serpedin, E.: Cost-unet: Convolution and swin transformer based deep learning architecture for cardiac segmentation. Biomed. Signal Process. Control 96, 106633 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"787_CR55","doi-asserted-by":"crossref","unstructured":"Rasool, N., Bhat, J.I., Wani, N.A., Ahmad, N., Alshara, M.: Transresunet: Revolutionizing glioma brain tumor segmentation through transformer-enhanced residual unet. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3402947"},{"key":"787_CR56","volume":"171","author":"W Zhang","year":"2024","unstructured":"Zhang, W., Chen, S., Ma, Y., Liu, Y., Cao, X.: Etunet: Exploring efficient transformer enhanced unet for 3d brain tumor segmentation. Comput. Biol. Med. 171, 108005 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"787_CR57","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11548-023-03024-8","volume":"19","author":"F Ghazouani","year":"2024","unstructured":"Ghazouani, F., Vera, P., Ruan, S.: Efficient brain tumor segmentation using swin transformer and enhanced local self-attention. Int. J. Comput. Assist. Radiol. Surg. 19(2), 273\u2013281 (2024)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"787_CR58","doi-asserted-by":"crossref","unstructured":"Nancy, A.M., Maheswari, R.: Brain tumor segmentation and classification using transfer learning based cnn model with model agnostic concept interpretation. Multimedia Tools and Applications, 1\u201330 (2024)","DOI":"10.1007\/s11042-024-20353-1"},{"key":"787_CR59","doi-asserted-by":"crossref","first-page":"72518","DOI":"10.1109\/ACCESS.2023.3289224","volume":"11","author":"A Jabbar","year":"2023","unstructured":"Jabbar, A., Naseem, S., Mahmood, T., Saba, T., Alamri, F.S., Rehman, A.: Brain tumor detection and multi-grade segmentation through hybrid caps-vggnet model. IEEE Access 11, 72518\u201372536 (2023)","journal-title":"IEEE Access"},{"issue":"1","key":"787_CR60","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12911-023-02174-8","volume":"23","author":"M Aggarwal","year":"2023","unstructured":"Aggarwal, M., Tiwari, A.K., Sarathi, M.P., Bijalwan, A.: An early detection and segmentation of brain tumor using deep neural network. BMC Med. Inform. Decis. Mak. 23(1), 78 (2023)","journal-title":"BMC Med. Inform. Decis. Mak."}],"updated-by":[{"DOI":"10.1007\/s44196-025-00807-6","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000}}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00787-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00787-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00787-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T08:07:13Z","timestamp":1744013233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00787-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["787"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00787-7","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s44196-025-00807-6","asserted-by":"object"}]},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,20]]},"assertion":[{"value":"1 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s44196-025-00807-6","URL":"https:\/\/doi.org\/10.1007\/s44196-025-00807-6","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Use of AI tools declaration"}}],"article-number":"63"}}