{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T18:30:03Z","timestamp":1768501803199,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819628810","type":"print"},{"value":"9789819628827","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2882-7_10","type":"book-chapter","created":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T06:59:25Z","timestamp":1741417165000},"page":"93-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LightMamba-UNet: Lightweight Mamba with\u00a0U-Net for\u00a0Efficient Skin Lesion Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2048-6864","authenticated-orcid":false,"given":"Wanzhen","family":"Hou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8357-2428","authenticated-orcid":false,"given":"Shiwei","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5300-0683","authenticated-orcid":false,"given":"Haifeng","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,9]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Aghdam, E.K., Azad, R., Zarvani, M., Merhof, D.: Attention swin U-net: cross-contextual attention mechanism for skin lesion segmentation. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230337"},{"issue":"6","key":"10_CR2","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.21037\/qims-19-1090","volume":"10","author":"S Cai","year":"2020","unstructured":"Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant. Imaging Med. Surg. 10(6), 1275 (2020)","journal-title":"Quant. Imaging Med. Surg."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"10_CR4","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Gao, Y., Zhou, M., Metaxas, D.N.: UTNet: a hybrid transformer architecture for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September\u20131 October 2021, Proceedings, Part III 24, pp. 61\u201371. Springer (2021)","DOI":"10.1007\/978-3-030-87199-4_6"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Hu, S., Liao, Z., Xia, Y.: Devil is in channels: contrastive single domain generalization for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 14\u201323. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_2"},{"issue":"3","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1016\/j.bbe.2020.07.007","volume":"40","author":"A Khanna","year":"2020","unstructured":"Khanna, A., Londhe, N.D., Gupta, S., Semwal, A.: A deep residual u-net convolutional neural network for automated lung segmentation in computed tomography images. Biocybern. Biomed. Eng. 40(3), 1314\u20131327 (2020)","journal-title":"Biocybern. Biomed. Eng."},{"key":"10_CR8","unstructured":"Liao, W., Zhu, Y., Wang, X., Pan, C., Wang, Y., Ma, L.: LightM-UNet: mamba assists in lightweight UNet for medical image segmentation. arXiv preprint arXiv:2403.05246 (2024)"},{"key":"10_CR9","first-page":"1","volume":"71","author":"A Lin","year":"2022","unstructured":"Lin, A., Chen, B., Xu, J., Zhang, Z., Lu, G., Zhang, D.: Ds-transunet: dual swin transformer u-net for medical image segmentation. IEEE Trans. Instrum. Meas. 71, 1\u201315 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10_CR10","unstructured":"Liu, Y., et al.: VMamba: visual state space model 2024. arXiv preprint arXiv:2401.10166 (2024)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10_CR12","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"10_CR13","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"10_CR14","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., 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 (EMBC), pp. 5437\u20135440. IEEE (2013)","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"10_CR16","unstructured":"Oktay, O., et\u00a0al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"10_CR17","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32 (2019)"},{"key":"10_CR18","unstructured":"Peng, Y., Sonka, M., Chen, D.Z.: U-net v2: rethinking the skip connections of U-net for medical image segmentation. arXiv preprint arXiv:2311.17791 (2023)"},{"key":"10_CR19","first-page":"10353","volume":"35","author":"Y Rao","year":"2022","unstructured":"Rao, Y., Zhao, W., Tang, Y., Zhou, J., Lim, S.N., Lu, J.: HorNet: efficient high-order spatial interactions with recursive gated convolutions. Adv. Neural. Inf. Process. Syst. 35, 10353\u201310366 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR20","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, 5\u20139 October 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10_CR21","unstructured":"Ruan, J., Xiang, S.: VM-UNet: vision mamba UNet for medical image segmentation. arXiv preprint arXiv:2402.02491 (2024)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Ruan, J., Xiang, S., Xie, M., Liu, T., Fu, Y.: MALUNet: a multi-attention and light-weight UNet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1150\u20131156. IEEE (2022)","DOI":"10.1109\/BIBM55620.2022.9995040"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Tang, H., Cheng, L., Huang, G., Tan, Z., Lu, J., Wu, K.: Rotate to scan: UNet-like mamba with triplet SSM module for medical image segmentation. arXiv preprint arXiv:2403.17701 (2024)","DOI":"10.1007\/s11760-024-03484-8"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Patel, V.M.: UNext: MLP-based rapid medical image segmentation network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 23\u201333. Springer (2022)","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September\u20131 October 1 2021, Proceedings, Part I, pp. 699\u2013708. Springer (2021)","DOI":"10.1007\/978-3-030-87193-2_66"},{"key":"10_CR26","doi-asserted-by":"publisher","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","volume":"76","author":"H Wu","year":"2022","unstructured":"Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., Wen, Z.: Fat-net: feature adaptive transformers for automated skin lesion segmentation. Med. Image Anal. 76, 102327 (2022)","journal-title":"Med. Image Anal."},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhong, J., Wang, W., Wen, Z., Qin, J.: Precise yet efficient semantic calibration and refinement in convnets for real-time polyp segmentation from colonoscopy videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2916\u20132924 (2021)","DOI":"10.1609\/aaai.v35i4.16398"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Wu, R., et al.: MHorUNet: High-order spatial interaction UNet for skin lesion segmentation. Biomed. Signal Process. Control 88, 105517 (2024)","DOI":"10.1016\/j.bspc.2023.105517"},{"key":"10_CR29","unstructured":"Wu, R., Liu, Y., Liang, P., Chang, Q.: Ultralight VM-UNet: parallel vision mamba significantly reduces parameters for skin lesion segmentation. arXiv preprint arXiv:2403.20035 (2024)"},{"key":"10_CR30","doi-asserted-by":"publisher","first-page":"107798","DOI":"10.1016\/j.compbiomed.2023.107798","volume":"168","author":"R Wu","year":"2024","unstructured":"Wu, R., Lv, H., Liang, P., Cui, X., Chang, Q., Huang, X.: HSH-UNet: hybrid selective high order interactive U-shaped model for automated skin lesion segmentation. Comput. Biol. Med. 168, 107798 (2024)","journal-title":"Comput. Biol. Med."},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yu, Y., Jin, S., Gu, L., Ling, T., Tao, X.: VM-UNet-v2: rethinking vision mamba UNet for medical image segmentation. In: International Symposium on Bioinformatics Research and Applications, pp. 335\u2013346. Springer (2024)","DOI":"10.1007\/978-981-97-5128-0_27"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., Hu, Q.: TransFuse: fusing transformers and CNNs for medical image segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, Strasbourg, France, 27 September\u20131 October 2021, Proceedings, Part I, pp. 14\u201324. Springer (2021)","DOI":"10.1007\/978-3-030-87193-2_2"},{"issue":"6","key":"10_CR33","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Advances in Brain Inspired Cognitive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2882-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T05:38:02Z","timestamp":1747114682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2882-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819628810","9789819628827"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2882-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"9 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Inspired Cognitive Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hefei","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bics2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bics2024.dobell.me\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}