{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:50:56Z","timestamp":1780091456802,"version":"3.54.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"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":["61772319, 62272281, 62002200, 62202268"],"award-info":[{"award-number":["61772319, 62272281, 62002200, 62202268"]}],"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":["61772319, 62272281, 62002200, 62202268"],"award-info":[{"award-number":["61772319, 62272281, 62002200, 62202268"]}],"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":["61772319, 62272281, 62002200, 62202268"],"award-info":[{"award-number":["61772319, 62272281, 62002200, 62202268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08101-0","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T18:10:29Z","timestamp":1766686229000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["BAMN: boundary-aware mamba network for skin lesion segmentation"],"prefix":"10.1007","volume":"82","author":[{"given":"Lutong","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"issue":"1","key":"8101_CR1","first-page":"10","volume":"75","author":"RL Siegel","year":"2025","unstructured":"Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A (2025) Cancer statistics. Ca 75(1):10","journal-title":"Ca"},{"issue":"10151","key":"8101_CR2","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/S0140-6736(18)31559-9","volume":"392","author":"D Schadendorf","year":"2018","unstructured":"Schadendorf D, Van Akkooi AC, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S (2018) Melanoma. The Lancet 392(10151):971\u2013984","journal-title":"The Lancet"},{"issue":"2","key":"8101_CR3","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/JBHI.2019.2895803","volume":"23","author":"ME Celebi","year":"2019","unstructured":"Celebi ME, Codella N, Halpern A (2019) Dermoscopy image analysis: overview and future directions. IEEE J Biomed Health Inform 23(2):474\u2013478","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"8101_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/ijdpdd.ijdpdd_13_17","volume":"4","author":"B Nirmal","year":"2017","unstructured":"Nirmal B (2017) Dermatoscopy: physics and principles. Indian J Dermatopathol Diagn Dermatol 4(2):27\u201330","journal-title":"Indian J Dermatopathol Diagn Dermatol"},{"issue":"5","key":"8101_CR5","doi-asserted-by":"publisher","first-page":"352","DOI":"10.4103\/ijd.IJD_418_20","volume":"65","author":"A De","year":"2020","unstructured":"De A, Sarda A, Gupta S, Das S (2020) Use of artificial intelligence in dermatology. Indian J Dermatol 65(5):352\u2013357","journal-title":"Indian J Dermatol"},{"key":"8101_CR6","doi-asserted-by":"crossref","unstructured":"Kazerouni A, Aghdam EK, Heidari M, Azad R, Fayyaz M, Hacihaliloglu I, Merhof D (2022) Diffusion models for medical image analysis: A comprehensive survey. arXiv preprint arXiv:2211.07804","DOI":"10.1016\/j.media.2023.102846"},{"key":"8101_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102677","volume":"83","author":"A Gupta","year":"2023","unstructured":"Gupta A, Gehlot S, Goswami S, Motwani S, Gupta R, Faura \u00c1G, \u0160tepec D, Martin\u010di\u010d T, Azad R, Merhof D et al (2023) Segpc-2021: A challenge & dataset on segmentation of multiple myeloma plasma cells from microscopic images. Med Image Anal 83:102677","journal-title":"Med Image Anal"},{"key":"8101_CR8","doi-asserted-by":"crossref","unstructured":"Azad R, Aghdam EK, Rauland A, Jia Y, Avval AH, Bozorgpour A, Karimijafarbigloo S, Cohen JP, Adeli E, Merhof D (2024) Medical image segmentation review: The success of u-net. In: IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2024.3435571"},{"key":"8101_CR9","doi-asserted-by":"crossref","unstructured":"Zhu Y, Duan P, Hua Z, Li J (2025) Dbcg-med: diffusion-based bidirectional calibration and context guidance for medical image segmentation. International Journal of Machine Learning and Cybernetics, 1\u201320","DOI":"10.1007\/s13042-025-02767-x"},{"key":"8101_CR10","unstructured":"Al-Amri SS, Kalyankar NV et al (2010) Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020"},{"key":"8101_CR11","doi-asserted-by":"crossref","unstructured":"Preetha MMSJ, Suresh LP, Bosco MJ (2012) Image segmentation using seeded region growing. In: 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 576\u2013583. IEEE","DOI":"10.1109\/ICCEET.2012.6203897"},{"issue":"12","key":"8101_CR12","first-page":"234","volume":"3","author":"K Bhargavi","year":"2014","unstructured":"Bhargavi K, Jyothi S (2014) A survey on threshold based segmentation technique in image processing. Int J Innov Res Dev 3(12):234\u2013239","journal-title":"Int J Innov Res Dev"},{"key":"8101_CR13","unstructured":"Ciresan DC, Meier U, Masci J, Maria\u00a0Gambardella L, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-international Joint Conference on Artificial Intelligence, vol. 22, p. 1237. Citeseer"},{"issue":"7587","key":"8101_CR14","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484\u2013489","journal-title":"Nature"},{"key":"8101_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) 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. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8101_CR16","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"8101_CR17","doi-asserted-by":"crossref","unstructured":"Zhao X, Zhang L, Lu H (2021) Automatic polyp segmentation via multi-scale subtraction network. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 120\u2013130. Springer","DOI":"10.1007\/978-3-030-87193-2_12"},{"key":"8101_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123590","volume":"249","author":"Z Tang","year":"2024","unstructured":"Tang Z, Chen B, Zeng A, Liu M, Zhao S (2024) Progressive deep snake for instance boundary extraction in medical images. Expert Syst Appl 249:123590","journal-title":"Expert Syst Appl"},{"key":"8101_CR19","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"8101_CR20","doi-asserted-by":"crossref","unstructured":"Wang H, Cao P, Wang J, Zaiane OR (2022) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2441\u20132449","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"8101_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106634","volume":"124","author":"P Song","year":"2023","unstructured":"Song P, Yang Z, Li J, Fan H (2023) Dpctn: Dual path context-aware transformer network for medical image segmentation. Eng Appl Artif Intell 124:106634","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"8101_CR22","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TMI.2023.3291719","volume":"43","author":"Z Li","year":"2023","unstructured":"Li Z, Li Y, Li Q, Wang P, Guo D, Lu L, Jin D, Zhang Y, Hong Q (2023) Lvit: language meets vision transformer in medical image segmentation. IEEE Trans Med Imaging 43(1):96\u2013107","journal-title":"IEEE Trans Med Imaging"},{"key":"8101_CR23","doi-asserted-by":"crossref","unstructured":"Kalman RE (1960) A new approach to linear filtering and prediction problems","DOI":"10.1115\/1.3662552"},{"key":"8101_CR24","unstructured":"Gu A, Dao T (2023) Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752"},{"key":"8101_CR25","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X (2024) Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model. arXiv:2401.09417"},{"key":"8101_CR26","first-page":"103031","volume":"37","author":"Y Liu","year":"2024","unstructured":"Liu Y, Tian Y, Zhao Y, Yu H, Xie L, Wang Y, Ye Q, Jiao J, Liu Y (2024) Vmamba: Visual state space model. Adv Neural Inf Process Syst 37:103031\u2013103063","journal-title":"Adv Neural Inf Process Syst"},{"key":"8101_CR27","doi-asserted-by":"crossref","unstructured":"Ruan J, Li J, Xiang S (2024) Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491","DOI":"10.1145\/3767748"},{"key":"8101_CR28","unstructured":"Gutman D, Codella NC, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397"},{"key":"8101_CR29","unstructured":"Berseth M (2017) Isic 2017-skin lesion analysis towards melanoma detection. arXiv preprint arXiv:1703.00523"},{"key":"8101_CR30","doi-asserted-by":"crossref","unstructured":"Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2018) 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 (ISBI 2018), pp. 168\u2013172. IEEE","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"8101_CR31","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) 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 (EMBC), pp. 5437\u20135440. IEEE","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"8101_CR32","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"8101_CR33","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"8101_CR34","doi-asserted-by":"crossref","unstructured":"Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331. IEEE","DOI":"10.1109\/ITME.2018.00080"},{"issue":"12","key":"8101_CR35","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans Med Imaging 37(12):2663\u20132674","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"8101_CR36","doi-asserted-by":"publisher","first-page":"6753","DOI":"10.1007\/s40747-023-01095-3","volume":"9","author":"J Ouyang","year":"2023","unstructured":"Ouyang J, Liu S, Peng H, Garg H, Thanh DN (2023) Lea u-net: a u-net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation. Complex & Intelligent Systems 9(6):6753\u20136766","journal-title":"Complex & Intelligent Systems"},{"issue":"5","key":"8101_CR37","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1109\/JBHI.2021.3138024","volume":"26","author":"A Srivastava","year":"2021","unstructured":"Srivastava A, Jha D, Chanda S, Pal U, Johansen HD, Johansen D, Riegler MA, Ali S, Halvorsen P (2021) Msrf-net: a multi-scale residual fusion network for biomedical image segmentation. IEEE J Biomed Health Inform 26(5):2252\u20132263","journal-title":"IEEE J Biomed Health Inform"},{"key":"8101_CR38","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Proc Syst 30"},{"key":"8101_CR39","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 (2022) Class-aware adversarial transformers for medical image segmentation. Adv Neural Inf Process Syst 35:29582\u201329596","journal-title":"Adv Neural Inf Process Syst"},{"key":"8101_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107307","volume":"229","author":"J Zheng","year":"2023","unstructured":"Zheng J, Liu H, Feng Y, Xu J, Zhao L (2023) Casf-net: Cross-attention and cross-scale fusion network for medical image segmentation. Comput Methods Programs Biomed 229:107307","journal-title":"Comput Methods Programs Biomed"},{"key":"8101_CR41","first-page":"3965","volume":"34","author":"Z Dai","year":"2021","unstructured":"Dai Z, Liu H, Le QV, Tan M (2021) Coatnet: Marrying convolution and attention for all data sizes. Adv Neural Inf Process Syst 34:3965\u20133977","journal-title":"Adv Neural Inf Process Syst"},{"key":"8101_CR42","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"key":"8101_CR43","unstructured":"Gu A, Goel K, R\u00e9 C (2021) Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396"},{"key":"8101_CR44","first-page":"1474","volume":"33","author":"A Gu","year":"2020","unstructured":"Gu A, Dao T, Ermon S, Rudra A, R\u00e9 C (2020) Hippo: Recurrent memory with optimal polynomial projections. Adv Neural Inf Process Syst 33:1474\u20131487","journal-title":"Adv Neural Inf Process Syst"},{"key":"8101_CR45","doi-asserted-by":"crossref","unstructured":"Xing Z, Ye T, Yang Y, Liu G, Zhu L (2024) Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 578\u2013588. Springer","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"8101_CR46","unstructured":"Liao W, Zhu Y, Wang X, Pan C, Wang Y, Ma L (2024) Lightm-unet: Mamba assists in lightweight unet for medical image segmentation. arXiv preprint arXiv:2403.05246"},{"key":"8101_CR47","unstructured":"Ma J, Li F, Wang B (2024) U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722"},{"key":"8101_CR48","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. Ieee","DOI":"10.1109\/3DV.2016.79"},{"key":"8101_CR49","doi-asserted-by":"crossref","unstructured":"Wu R, Liu Y, Liang P, Chang Q (2024) Ultralight vm-unet: Parallel vision mamba significantly reduces parameters for skin lesion segmentation. arXiv preprint arXiv:2403.20035","DOI":"10.1016\/j.patter.2025.101298"},{"key":"8101_CR50","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 (2023) Dcsau-net: A deeper and more compact split-attention u-net for medical image segmentation. Comput Biol Med 154:106626","journal-title":"Comput Biol Med"},{"key":"8101_CR51","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM (2021) Medical transformer: Gated axial-attention for medical image segmentation. In: Medical Image Computing and Computer Assisted intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 36\u201346. Springer","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"8101_CR52","doi-asserted-by":"crossref","unstructured":"Shi W, Xu J, Gao P (2022) Ssformer: A lightweight transformer for semantic segmentation. In: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20135 IEEE","DOI":"10.1109\/MMSP55362.2022.9949177"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08101-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08101-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08101-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T18:10:35Z","timestamp":1766686235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08101-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,25]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["8101"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08101-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,25]]},"assertion":[{"value":"13 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2025","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 that no potential Conflict of interest exist. There is no an undisclosed relationship that may pose a Conflict of interest. There is no undisclosed funding source that may pose a Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"28"}}