{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T15:12:02Z","timestamp":1786374722838,"version":"build-2736575974"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"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":["No.12101426"],"award-info":[{"award-number":["No.12101426"]}],"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":["No.12426308"],"award-info":[{"award-number":["No.12426308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Outstanding Young Scientist Program","award":["No. JWZQ20240101027"],"award-info":[{"award-number":["No. JWZQ20240101027"]}]},{"name":"Beijing Natural Science Foundation","award":["L242127"],"award-info":[{"award-number":["L242127"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No.2020YFA0712203"],"award-info":[{"award-number":["No.2020YFA0712203"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02157-x","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:11:17Z","timestamp":1786371077000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Geometric Information Propagation for Semi-supervised 3D Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Lianyuan","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuzhen","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1890-1520","authenticated-orcid":false,"given":"Ji","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"2157_CR1","doi-asserted-by":"crossref","unstructured":"Luo X, Chen J, Song T, Wang G. Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI conference on artificial intelligence. vol. 35; 2021. p. 8801\u20138809.","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"2157_CR2","doi-asserted-by":"crossref","unstructured":"Li S, Zhang C, He X. Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23. Springer; 2020. p. 552\u2013561.","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"2157_CR3","doi-asserted-by":"crossref","unstructured":"Xu Z, Wang Y, Lu D, Luo X, Yan J, Zheng Y, et al. Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation. Medical Image Analysis. 2023;88:102880.","DOI":"10.1016\/j.media.2023.102880"},{"key":"2157_CR4","doi-asserted-by":"crossref","unstructured":"Luo X, Wang G, Liao W, Chen J, Song T, Chen Y, et al. Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Medical Image Analysis. 2022;80:102517.","DOI":"10.1016\/j.media.2022.102517"},{"key":"2157_CR5","doi-asserted-by":"crossref","unstructured":"Gao S, Zhang Z, Ma J, Li Z, Zhang S. Correlation-aware mutual learning for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2023. p. 98\u2013108.","DOI":"10.1007\/978-3-031-43907-0_10"},{"key":"2157_CR6","doi-asserted-by":"crossref","unstructured":"Guo X, Shi J, Li Z, Yu L, Lei N, Li H, et al. Semi-Supervised Medical Image Segmentation Based on Local Consistency and Volume Constancy. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2024. p. 1\u20135.","DOI":"10.1109\/ISBI56570.2024.10635807"},{"key":"2157_CR7","doi-asserted-by":"crossref","unstructured":"Xia Y, Yang D, Yu Z, Liu F, Cai J, Yu L, et al. Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Medical image analysis. 2020;65:101766.","DOI":"10.1016\/j.media.2020.101766"},{"key":"2157_CR8","doi-asserted-by":"crossref","unstructured":"Lei T, Zhang D, Du X, Wang X, Wan Y, Nandi AK. Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network. IEEE transactions on medical imaging. 2022;42(5):1265\u20131277.","DOI":"10.1109\/TMI.2022.3225687"},{"key":"2157_CR9","doi-asserted-by":"crossref","unstructured":"Chen C, Zhou K, Wang Z, Xiao R. Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA. IEEE Transactions on Medical Imaging. 2022;42(2):346\u2013353.","DOI":"10.1109\/TMI.2022.3184675"},{"key":"2157_CR10","unstructured":"Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems. 2017;30."},{"key":"2157_CR11","doi-asserted-by":"crossref","unstructured":"Cui W, Liu Y, Li Y, Guo M, Li Y, Li X, et al. Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2\u20137, 2019, Proceedings 26. Springer; 2019. p. 554\u2013565.","DOI":"10.1007\/978-3-030-20351-1_43"},{"key":"2157_CR12","doi-asserted-by":"crossref","unstructured":"Song Y, Zhang C, Lee J, Wang F, Xiang S, Zhang D. Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images. Pattern analysis and applications. 2009;12:99\u2013115.","DOI":"10.1007\/s10044-008-0104-3"},{"key":"2157_CR13","doi-asserted-by":"crossref","unstructured":"Santhirasekaram A, Winkler M, Rockall A, Glocker B. A geometric approach to robust medical image segmentation. Medical Image Analysis. 2024;97:103260.","DOI":"10.1016\/j.media.2024.103260"},{"key":"2157_CR14","doi-asserted-by":"crossref","unstructured":"Wang K, Zhang X, Lu Y, Zhang W, Huang S, Yang D. GSAL: Geometric structure adversarial learning for robust medical image segmentation. Pattern Recognition. 2023;140:109596.","DOI":"10.1016\/j.patcog.2023.109596"},{"key":"2157_CR15","doi-asserted-by":"crossref","unstructured":"Meng Y, Zhang Y, Xie J, Duan J, Joddrell M, Madhusudhan S, et al. Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment. Medical Image Analysis. 2024;95:103183.","DOI":"10.1016\/j.media.2024.103183"},{"key":"2157_CR16","doi-asserted-by":"crossref","unstructured":"Bai Y, Chen D, Li Q, Shen W, Wang Y. Bidirectional copy-paste for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2023. p. 11514\u201311524.","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"2157_CR17","doi-asserted-by":"crossref","unstructured":"Seibold CM, Rei\u00df S, Kleesiek J, Stiefelhagen R. Reference-guided pseudo-label generation for medical semantic segmentation. In: Proceedings of the AAAI conference on artificial intelligence. vol. 36; 2022. p. 2171\u20132179.","DOI":"10.1609\/aaai.v36i2.20114"},{"key":"2157_CR18","doi-asserted-by":"crossref","unstructured":"Iscen A, Tolias G, Avrithis Y, Chum O. Label propagation for deep semi-supervised learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2019. p. 5070\u20135079.","DOI":"10.1109\/CVPR.2019.00521"},{"key":"2157_CR19","doi-asserted-by":"crossref","unstructured":"Zhu L, Zhou J, Goh RSM, Liu Y. AdvMIM: Adversarial Masked Image Modeling for Semi-supervised Medical Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2025. p. 56\u201366.","DOI":"10.1007\/978-3-032-05325-1_6"},{"key":"2157_CR20","doi-asserted-by":"crossref","unstructured":"Peng Z, Wang G, Xu Z, Yang X, Shen W. DMformer: Difficulty-adapted Masked Transformer for Semi-Supervised Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics. 2025;.","DOI":"10.1109\/JBHI.2025.3637281"},{"key":"2157_CR21","doi-asserted-by":"crossref","unstructured":"Wang Y, Li Z, Mei J, Wei Z, Liu L, Wang C, et al. Swinmm: masked multi-view with swin transformers for 3d medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2023. p. 486\u2013496.","DOI":"10.1007\/978-3-031-43898-1_47"},{"key":"2157_CR22","doi-asserted-by":"crossref","unstructured":"Xu S, Tian L. DiffCNN: A collaborative framework of diffusion model and CNN for semi-supervised medical image segmentation. Neural Networks. 2025;191:107813.","DOI":"10.1016\/j.neunet.2025.107813"},{"key":"2157_CR23","doi-asserted-by":"crossref","unstructured":"Yu L, Guo X, Zhao X, Li H. A Diffusion and Spatial Prototype Learning Method for Semi-Supervised Medical Image Segmentation. In: 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2025. p. 4359\u20134364.","DOI":"10.1109\/BIBM66473.2025.11356306"},{"key":"2157_CR24","doi-asserted-by":"crossref","unstructured":"Liu X, Li W, Yuan Y. Diffrect: Latent diffusion label rectification for semi-supervised medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2024. p. 56\u201366.","DOI":"10.1007\/978-3-031-72390-2_6"},{"key":"2157_CR25","doi-asserted-by":"crossref","unstructured":"Yao Y, Duan X, Qu A, Chen M, Chen J, Chen L. DFCG: A Dual-Frequency Cascade Graph model for semi-supervised ultrasound image segmentation with diffusion model. Knowledge-Based Systems. 2024;300:112261.","DOI":"10.1016\/j.knosys.2024.112261"},{"key":"2157_CR26","doi-asserted-by":"crossref","unstructured":"Wang H, Li X. Towards generic semi-supervised framework for volumetric medical image segmentation. Advances in Neural Information Processing Systems. 2023;36:1833\u20131848.","DOI":"10.52202\/075280-0088"},{"key":"2157_CR27","doi-asserted-by":"publisher","unstructured":"Huang K, Zhou T, Fu H, Zhang Y, Zhou Y, Gong C, et al. Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging. 2025;44(5):2295\u20132306. https:\/\/doi.org\/10.1109\/TMI.2025.3530097.","DOI":"10.1109\/TMI.2025.3530097"},{"key":"2157_CR28","doi-asserted-by":"publisher","unstructured":"Li S, Qi L, Yu Q, Huo J, Shi Y, Gao Y. Stitching, Fine-Tuning, and Re-Training: A SAM-Enabled Framework for Semi-Supervised 3D Medical Image Segmentation. IEEE Transactions on Medical Imaging. 2025;44(10):3909\u20133923. https:\/\/doi.org\/10.1109\/TMI.2025.3532084.","DOI":"10.1109\/TMI.2025.3532084"},{"key":"2157_CR29","doi-asserted-by":"publisher","unstructured":"Zhang Y, Lv B, Xue L, Zhang W, Liu Y, Fu Y, et al. SemiSAM+: Rethinking semi-supervised medical image segmentation in the era of foundation models. Medical Image Analysis. 2025;106:103733. https:\/\/doi.org\/10.1016\/j.media.2025.103733.","DOI":"10.1016\/j.media.2025.103733"},{"key":"2157_CR30","doi-asserted-by":"publisher","unstructured":"Qin C, Chen Z, Wang D, Zheng B, Luo J, Zeng J, et al. DUR-Net+: Semi-Supervised Abdominal CT Pheochromocytoma Segmentation Via Dynamic Uncertainty Rectified and Prior Knowledge From SAM-Med3D. IEEE Journal of Biomedical and Health Informatics. 2025;p. 1\u201314. https:\/\/doi.org\/10.1109\/JBHI.2025.3594897.","DOI":"10.1109\/JBHI.2025.3594897"},{"key":"2157_CR31","doi-asserted-by":"crossref","unstructured":"Lu W, Hong Y, Yang Y. UP-SAM: Uncertainty-Informed Adaptation of Segment Anything Model for Semi-Supervised Medical Image Segmentation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2024. p. 2256\u20132261.","DOI":"10.1109\/BIBM62325.2024.10822398"},{"key":"2157_CR32","doi-asserted-by":"crossref","unstructured":"He W, Zhang C, Dai J, Liu L, Wang T, Liu X, et al. A statistical deformation model-based data augmentation method for volumetric medical image segmentation. Medical image analysis. 2024;91:102984.","DOI":"10.1016\/j.media.2023.102984"},{"key":"2157_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Y, Chen Q, Huang C, Zhang Z, Chen M, Fu Z. Intuitive axial augmentation using polar-sine-based piecewise distortion for medical slice-wise segmentation. Smart Health. 2025;36:100556.","DOI":"10.1016\/j.smhl.2025.100556"},{"key":"2157_CR34","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, et al. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18. Springer; 2015. p. 556\u2013564.","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"2157_CR35","doi-asserted-by":"crossref","unstructured":"Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, et al. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical image analysis. 2021;67:101832.","DOI":"10.1016\/j.media.2020.101832"},{"key":"2157_CR36","unstructured":"Bakas SS.: Brats MICCAI Brain tumor dataset. IEEE Dataport. Available from: https:\/\/dx.doi.org\/10.21227\/hdtd-5j88."},{"key":"2157_CR37","unstructured":"Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems. 2017;30."},{"key":"2157_CR38","unstructured":"Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv:2312.00752. 2023;."},{"key":"2157_CR39","doi-asserted-by":"crossref","unstructured":"Xing Z, Ye T, Yang Y, Liu G, Zhu L. Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2024. p. 578\u2013588.","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"2157_CR40","unstructured":"Gu A. Modeling Sequences with Structured State Spaces. Stanford University; 2023."},{"key":"2157_CR41","doi-asserted-by":"crossref","unstructured":"Behjati P, Rodriguez P, Fern\u00e1ndez C, Hupont I, Mehri A, Gonzalez J. Single image super-resolution based on directional variance attention network. Pattern Recognition. 2023;133:108997.","DOI":"10.1016\/j.patcog.2022.108997"},{"key":"2157_CR42","doi-asserted-by":"publisher","unstructured":"Guo X, Yu L, Shi J, Wang H, Zhao J, Zhang R, et al. Optimal Transport and Central Moment Consistency Regularization for Semi-supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging. 2025;p. 1\u20131. https:\/\/doi.org\/10.1109\/TMI.2025.3563500.","DOI":"10.1109\/TMI.2025.3563500"},{"key":"2157_CR43","doi-asserted-by":"crossref","unstructured":"Yu L, Wang S, Li X, Fu CW, Heng PA. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2019: 22nd international conference, Shenzhen, China, October 13\u201317, 2019, proceedings, part II 22. Springer; 2019. p. 605\u2013613.","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"2157_CR44","doi-asserted-by":"crossref","unstructured":"Wu Y, Xu M, Ge Z, Cai J, Zhang L. Semi-supervised left atrium segmentation with mutual consistency training. In: Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, Strasbourg, France, September 27\u2013October 1, 2021, proceedings, part II 24. Springer; 2021. p. 297\u2013306.","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"2157_CR45","doi-asserted-by":"crossref","unstructured":"Wu Y, Ge Z, Zhang D, Xu M, Zhang L, Xia Y, et al. Mutual consistency learning for semi-supervised medical image segmentation. Medical Image Analysis. 2022;81:102530.","DOI":"10.1016\/j.media.2022.102530"},{"key":"2157_CR46","doi-asserted-by":"crossref","unstructured":"Su J, Luo Z, Lian S, Lin D, Li S. Mutual learning with reliable pseudo label for semi-supervised medical image segmentation. Medical Image Analysis. 2024;94:103111.","DOI":"10.1016\/j.media.2024.103111"},{"key":"2157_CR47","doi-asserted-by":"publisher","unstructured":"Yuan Y, Wang X, Yang X, Heng PA. Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning. IEEE Transactions on Medical Imaging. 2025;44(3):1181\u20131193. https:\/\/doi.org\/10.1109\/TMI.2024.3484166.","DOI":"10.1109\/TMI.2024.3484166"},{"key":"2157_CR48","doi-asserted-by":"crossref","unstructured":"Yang Y, Sun G, Zhang T, Wang R, Su J. Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation. Medical Image Analysis. 2025;101:103450.","DOI":"10.1016\/j.media.2024.103450"},{"key":"2157_CR49","doi-asserted-by":"crossref","unstructured":"Wang M, Li J, Su H, Yin N, Yang L, Li S. GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation. arXiv:2411.13147. 2024;.","DOI":"10.2139\/ssrn.4909442"},{"key":"2157_CR50","doi-asserted-by":"crossref","unstructured":"Yan K, Cai Q, Zhang F, Cao Z, Liu Z. SGTC: Semantic-guided triplet co-training for sparsely annotated semi-supervised medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 39; 2025. p. 9112\u20139120.","DOI":"10.1609\/aaai.v39i9.32986"},{"key":"2157_CR51","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J, Liu Y, Cheng Y, Qi Y. SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2024. p. 3982\u20133986.","DOI":"10.1109\/BIBM62325.2024.10821951"},{"key":"2157_CR52","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). Ieee; 2016. p. 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"2157_CR53","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment Anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV); 2023. p. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"2157_CR54","doi-asserted-by":"crossref","unstructured":"Wang H, Guo S, Ye J, Deng Z, Cheng J, Li T, et al. SAM-Med3D: Towards General-Purpose Segmentation Models for Volumetric Medical Images. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops. Springer; 2025. p. 51\u201367.","DOI":"10.1007\/978-3-031-91721-9_4"},{"key":"2157_CR55","doi-asserted-by":"crossref","unstructured":"K\u0131l\u0131\u00e7 \u015e. Enhanced Vertebra-GNN: A Graph-Based Deep Learning Framework for Anatomically Informed Spinal Disease Classification. Journal of Imaging Informatics in Medicine. 2025;p. 1\u201316.","DOI":"10.1007\/s10278-025-01696-z"},{"key":"2157_CR56","doi-asserted-by":"crossref","unstructured":"K\u0131l\u0131\u00e7 \u015e, Do\u011fan Y. A pruned and parameter-efficient Xception framework for skin cancer classification. Plos one. 2026;21(3):e0341227.","DOI":"10.1371\/journal.pone.0341227"},{"key":"2157_CR57","doi-asserted-by":"crossref","unstructured":"Huang H, Chen Z, Zou Y, Lu M, Chen C, Song Y, et al. Channel prior convolutional attention for medical image segmentation. Computers in Biology and Medicine. 2024;178:108784.","DOI":"10.1016\/j.compbiomed.2024.108784"},{"key":"2157_CR58","doi-asserted-by":"crossref","unstructured":"Song E, Zhan B, Liu H. Combining external-latent attention for medical image segmentation. Neural Networks. 2024;170:468\u2013477.","DOI":"10.1016\/j.neunet.2023.10.046"},{"key":"2157_CR59","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yin J, Gu Y, Chen Y. Multi-level Feature Attention Network for medical image segmentation. Expert Systems with Applications. 2025;263:125785.","DOI":"10.1016\/j.eswa.2024.125785"},{"key":"2157_CR60","doi-asserted-by":"crossref","unstructured":"Rahman MM, Marculescu R. Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision; 2023. p. 6222\u20136231.","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"2157_CR61","doi-asserted-by":"crossref","unstructured":"K\u0131l\u0131\u00e7 \u015e. Attention-based dual-path deep learning for blood cell image classification using ConvNeXt and swin transformer. Journal of Imaging Informatics in Medicine. 2026;39(1):564\u2013582.","DOI":"10.1007\/s10278-025-01479-6"},{"key":"2157_CR62","doi-asserted-by":"crossref","unstructured":"K\u0131l\u0131\u00e7 \u015e. Densenet201+ with multi-scale attention and deep feature engineering for automated Kellgren\u2013Lawrence grading of knee osteoarthritis. PeerJ Computer Science. 2025;11:e3329.","DOI":"10.7717\/peerj-cs.3329"},{"key":"2157_CR63","doi-asserted-by":"crossref","unstructured":"Ruan J, Li J, Xiang S. Vm-unet: Vision mamba unet for medical image segmentation. arXiv:2402.02491. 2024;.","DOI":"10.1145\/3767748"},{"key":"2157_CR64","unstructured":"Wang Z, Ma C. Weak-mamba-unet: Visual mamba makes cnn and vit work better for scribble-based medical image segmentation. arXiv:2402.10887. 2024;."},{"key":"2157_CR65","unstructured":"Fan C, Yu H, Huang Y, Wang L, Yang Z, Jia X. SliceMamba with Neural Architecture Search for Medical Image Segmentation. arXiv:2407.08481. 2024;."},{"key":"2157_CR66","unstructured":"Zou S, Zhang M, Fan B, Zhou Z, Zou X. SkinMamba: A Precision Skin Lesion Segmentation Architecture with\u00a0Cross-Scale Global State Modeling and Frequency Boundary Guidance. arXiv:2409.10890. 2024;."},{"key":"2157_CR67","doi-asserted-by":"crossref","unstructured":"Chen Z, Bian Y, Shen E, Fan L, Zhu W, Shi F, et al. Moment-Consistent Contrastive CycleGAN for Cross-Domain Pancreatic Image Segmentation. IEEE Transactions on Medical Imaging. 2024;.","DOI":"10.1109\/TMI.2024.3447071"},{"key":"2157_CR68","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI brainlesion workshop. Springer; 2021. p. 272\u2013284.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"2157_CR69","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2016. p. 424\u2013432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"2157_CR70","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Advances in neural information processing systems. 2017;30."},{"key":"2157_CR71","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021;18(2):203\u2013211.","DOI":"10.1038\/s41592-020-01008-z"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02157-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02157-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02157-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:11:29Z","timestamp":1786371089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02157-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,10]]},"references-count":71,"alternative-id":["2157"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02157-x","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,10]]},"assertion":[{"value":"28 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study does not involve human or animal subjects.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study does not involve human participants.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This manuscript does not contain any data related to an identifiable individual, including personal details, images, or videos.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":4,"name":"Ethics","label":"Competing interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}