{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:58:18Z","timestamp":1768993098996,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556335","type":"print"},{"value":"9789819556342","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5634-2_6","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:03Z","timestamp":1768944183000},"page":"76-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["U-MLLA: A Cognitive-Inspired Enhancement of\u00a0Linear Attention for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yufeng","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongxi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., et al.: Beyond self-attention: deformable large kernel attention for medical image segmentation. In: Proceedings of the IEEE\/CVF WACV, pp. 1287\u20131297 (2024)","DOI":"10.1109\/WACV57701.2024.00132"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Bernard, O., Lalande, A., et\u00a0al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE TMI 37(11), 2514\u20132525 (2018)","DOI":"10.1109\/TMI.2018.2837502"},{"key":"6_CR3","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: ECCV Workshops (2021)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C., Yu, L., Min, S., Wang, S.: MSVM-Unet: multi-scale vision mamba UNet for medical image segmentation. arXiv preprint arXiv: 2408.13735 (2024)","DOI":"10.1109\/BIBM62325.2024.10821761"},{"key":"6_CR5","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"6_CR6","unstructured":"Chu, X., Tian, Z., Zhang, B., Wang, X., Shen, C.: Conditional positional encodings for vision transformers. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, 1\u20135 May 2023. OpenReview.net (2023). https:\/\/openreview.net\/forum?id=3KWnuT-R1bh"},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1146\/annurev.ne.18.030195.001205","volume":"18","author":"R Desimone","year":"1995","unstructured":"Desimone, R., Duncan, J., et al.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18(1), 193\u2013222 (1995)","journal-title":"Annu. Rev. Neurosci."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: CSWin transformer: a general vision transformer backbone with cross-shaped windows. In: Proceedings of the IEEE\/CVF CVPR, pp. 12124\u201312134 (2022)","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"6_CR9","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv: 2312.00752 (2023)"},{"key":"6_CR10","unstructured":"Han, D., et al.: Demystify mamba in vision: a linear attention perspective. arXiv preprint arXiv:2405.16605 (2024)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: IEEE\/CVF WACV 2022, Waikoloa, HI, USA, 3\u20138 January 2022, pp. 1748\u20131758. IEEE (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"6_CR12","unstructured":"Huang, Z., et al.: STU-Net: scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training. arXiv preprint arXiv: 2304.06716 (2023)"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nNU-Net revisited: a call for rigorous validation in 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 488\u2013498. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"6_CR15","unstructured":"Ji, Y., et al.: AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. In: Advances in Neural Information Processing Systems, vol. 35, pp. 36722\u201336732 (2022)"},{"key":"6_CR16","unstructured":"Jiang, Y., et al.: M$$^{3}$$ AD: multi-task multi-gate mixture of experts for Alzheimer\u2019s disease diagnosis with conversion pattern modeling. arXiv preprint arXiv:2508.01819 (2025)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Shen, Y.: M4oE: a foundation model for medical multimodal image segmentation with mixture of experts. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 621\u2013631. Springer (2024)","DOI":"10.1007\/978-3-031-72390-2_58"},{"key":"6_CR18","unstructured":"Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: MICCAI multi-atlas labeling beyond the cranial vault\u2013workshop and challenge. In: Proceedings MICCAI Multi-Atlas Labeling Beyond Cranial Vault\u2013Workshop Challenge, vol.\u00a05, p.\u00a012 (2015)"},{"issue":"11","key":"6_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"6_CR20","unstructured":"Liu, Y., et al.: VMamba: visual state space model. arXiv preprint arXiv: 2401.10166 (2024)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Luo, X., et al.: Word: a large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. arXiv preprint arXiv:2111.02403 (2021)","DOI":"10.1016\/j.media.2022.102642"},{"key":"6_CR22","unstructured":"Ma, J., Li, F., Wang, B.: U-Mamba: enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, Y., Gu, S., Ge, C., Ma, S., et\u00a0al.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the FLARE22 challenge. arXiv preprint arXiv:2308.05862 (2023)","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Manning, D.: Cognitive factors in reading medical images: thinking processes in image interpretation. In: The Handbook of Medical Image Perception and Techniques, pp. 107\u2013120 (2009)","DOI":"10.1017\/9781108163781.009"},{"issue":"5","key":"6_CR25","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3390\/data8050079","volume":"8","author":"F Quinton","year":"2023","unstructured":"Quinton, F., et al.: A tumour and liver automatic segmentation (ATLAS) dataset on contrast-enhanced magnetic resonance imaging for hepatocellular carcinoma. Data 8(5), 79 (2023)","journal-title":"Data"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Marculescu, R.: G-CASCADE: efficient cascaded graph convolutional decoding for 2D medical image segmentation. In: Proceedings of the IEEE\/CVF WACV, pp. 7728\u20137737 (2024)","DOI":"10.1109\/WACV57701.2024.00755"},{"key":"6_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"6_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063","volume":"568","author":"J Su","year":"2024","unstructured":"Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., Liu, Y.: RoFormer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2023.127063","journal-title":"Neurocomputing"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Sun, M., Jiang, Y., Guo, H.: Semi-supervised detection, identification and segmentation for abdominal organs. In: MICCAI Challenge on Fast and Low-Resource Semi-Supervised Abdominal Organ Segmentation, pp. 35\u201346. Springer (2022)","DOI":"10.1007\/978-3-031-23911-3_4"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of Swin transformers for 3D medical image analysis. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"6_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"6_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5634-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:06Z","timestamp":1768944186000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5634-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556335","9789819556342"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5634-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}