{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:32:16Z","timestamp":1775316736709,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556304","type":"print"},{"value":"9789819556311","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\/978-981-95-5631-1_17","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:08:18Z","timestamp":1769497698000},"page":"234-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EMM-UNet: An Edge-Enhanced and\u00a0Multi-scale Model Based on\u00a0Mamba for\u00a0Skin Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Lifang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Entao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunmin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"issue":"8","key":"17_CR1","doi-asserted-by":"publisher","first-page":"4315","DOI":"10.1007\/s11042-024-18895-5","volume":"84","author":"R Kaur","year":"2025","unstructured":"Kaur, R., Kaur, S.: Automatic skin lesion segmentation using attention residual U-net with improved encoder-decoder architecture. Multimed. Tools Appl. 84(8), 4315\u20134341 (2025)","journal-title":"Multimed. Tools Appl."},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Kratzer, T.B., Giaquinto, A.N., Sung, H., Jemal, A.: Cancer statistics, 2025. CA 75(1), 10 (2025)","DOI":"10.3322\/caac.21871"},{"key":"17_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117069","volume":"201","author":"R Wang","year":"2022","unstructured":"Wang, R., Chen, S., Ji, C., Li, Y.: Cascaded context enhancement network for automatic skin lesion segmentation. Expert Syst. Appl. 201, 117069 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"17_CR4","doi-asserted-by":"publisher","first-page":"781","DOI":"10.2174\/1573405616666200129095242","volume":"16","author":"N Razmjooy","year":"2020","unstructured":"Razmjooy, N., et al.: Computer-aided diagnosis of skin cancer: a review. Curr. Med. Imaging Rev. 16(7), 781\u2013793 (2020)","journal-title":"Curr. Med. Imaging Rev."},{"key":"17_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106343","volume":"152","author":"Q Han","year":"2023","unstructured":"Han, Q., et al.: HWA-SegNet: multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment. Comput. Biol. Med. 152, 106343 (2023)","journal-title":"Comput. Biol. Med."},{"key":"17_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-981-97-8499-8_12","volume-title":"Pattern Recognition and Computer Vision - PRCV 2024","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Wu, J., Wang, D.-H., Zhang, X., Zhu, S.: Bridge the gap of semantic context: a boundary-guided context fusion UNet for medical image segmentation. In: Lin, Z., et al. (eds.) PRCV 2024. LNCS, vol. 15045, pp. 165\u2013179. Springer, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-97-8499-8_12"},{"key":"17_CR7","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"17_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108671","volume":"178","author":"H Wu","year":"2024","unstructured":"Wu, H., et al.: HD-former: a hierarchical dependency transformer for medical image segmentation. Comput. Biol. Med. 178, 108671 (2024)","journal-title":"Comput. Biol. Med."},{"key":"17_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109524","volume":"139","author":"Y Sun","year":"2023","unstructured":"Sun, Y., Dai, D., Zhang, Q., Wang, Y., Xu, S., Lian, C.: MSCA-net: multi-scale contextual attention network for skin lesion segmentation. Pattern Recogn. 139, 109524 (2023)","journal-title":"Pattern Recogn."},{"key":"17_CR10","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: VMamba: visual state space model. In: Advances in Neural Information Processing Systems, vol. 37, pp. 103031\u2013103063 (2024)","DOI":"10.52202\/079017-3273"},{"issue":"13","key":"17_CR12","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.3390\/app14135683","volume":"14","author":"H Zhang","year":"2024","unstructured":"Zhang, H., et al.: A survey on visual mamba. Appl. Sci. 14(13), 5683 (2024)","journal-title":"Appl. Sci."},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Ruan, J., Li, J., Xiang, S.: VM-Unet: vision Mamba Unet for medical image segmentation. arXiv preprint arXiv:2402.02491 (2024)","DOI":"10.1145\/3767748"},{"key":"17_CR14","unstructured":"Zou, S., Zhang, M., Fan, B., Zhou, Z., Zou, X.: SkinMamba: a precision skin lesion segmentation architecture with cross-scale global state modeling and frequency boundary guidance. arXiv preprint arXiv:2409.10890 (2024)"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Wu, R., Liu, Y., Liang, P., Chang, Q.: H-VMUnet: high-order vision mamba Unet for medical image segmentation. Neurocomputing, 129447 (2025)","DOI":"10.1016\/j.neucom.2025.129447"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Fan, C., Yu, H., Huang, Y., Wang, L., Yang, Z., Jia, X.: SliceMamba with neural architecture search for medical image segmentation. IEEE J. Biomed. Health Inform. (2025)","DOI":"10.1109\/JBHI.2025.3564381"},{"issue":"12","key":"17_CR17","doi-asserted-by":"publisher","first-page":"14956","DOI":"10.1109\/TPAMI.2023.3300513","volume":"45","author":"Z Su","year":"2023","unstructured":"Su, Z., et al.: Lightweight pixel difference networks for efficient visual representation learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 14956\u201314974 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 168\u2013172. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"17_CR20","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Mar\u00e7al, A.R.S., Rozeira, J.: PH 2-a dermoscopic image database for research and benchmarking. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437\u20135440. IEEE (2013)","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"17_CR22","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":"17_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/978-3-031-21014-3_39","volume-title":"Machine Learning in Medical Imaging - MLMI 2022","author":"R Azad","year":"2022","unstructured":"Azad, R., Heidari, M., Wu, Y., Merhof, D.: Contextual attention network: transformer meets u-net. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) MLMI 2022. LNCS, vol. 13583, pp. 377\u2013386. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21014-3_39"},{"key":"17_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Ma, Z., Duan, W., et al.: DCSAU-net: a deeper and more compact split-attention u-net for medical image segmentation. Comput. Biol. Med. 154, 106626 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"17_CR25","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TETCI.2023.3309626","volume":"8","author":"B Chen","year":"2023","unstructured":"Chen, B., Liu, Y., Zhang, Z., Lu, G., Kong, A.W.K.: Transattunet: multi-level attention-guided U-net with transformer for medical image segmentation. IEEE Trans. Emerg. Top. Comput. Intell. 8(1), 55\u201368 (2023)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"17_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107627","volume":"167","author":"L Liu","year":"2023","unstructured":"Liu, L., Li, Y., Wu, Y., Ren, L., Wang, G.: LGI net: enhancing local-global information interaction for medical image segmentation. Comput. Biol. Med. 167, 107627 (2023)","journal-title":"Comput. Biol. Med."},{"key":"17_CR27","unstructured":"Li, H., et al.: Enhancing medical image segmentation with MA-UNet: a multi-scale attention framework. Vis. Comput. 1\u201318 (2025)"}],"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\/978-981-95-5631-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:43:28Z","timestamp":1775313808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5631-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556304","9789819556311"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5631-1_17","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":"28 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"}}]}}