{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:37:44Z","timestamp":1771328264130,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"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":["Prog Artif Intell"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s13748-023-00300-1","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T09:02:38Z","timestamp":1677488558000},"page":"99-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SwinE-UNet3+: swin transformer encoder network for medical image segmentation"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1354-3982","authenticated-orcid":false,"given":"Ping","family":"Zou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1201-5915","authenticated-orcid":false,"given":"Jian-Sheng","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"issue":"4","key":"300_CR1","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/CVPR.2015.7298965","volume":"39","author":"J Long","year":"2015","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"300_CR2","doi-asserted-by":"publisher","unstructured":"Milletari, F., Navab, N., Ahmadi, S. A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016). https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"300_CR3","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"300_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020). https:\/\/doi.org\/10.1016\/j.neunet.2019.08.025","journal-title":"Neural Netw."},{"issue":"12","key":"300_CR5","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Hao, C., Qi, X., Qi, D., Fu, C.W., Pheng-Ann, H.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"300_CR6","doi-asserted-by":"publisher","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., Simulamet.: Resunet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225\u20132255 (2019). https:\/\/doi.org\/10.1109\/ISM46123.2019.00049","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"300_CR7","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"300_CR8","doi-asserted-by":"publisher","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mcdonagh, S., Hammerla, N.Y., Kainz, B.: Attention U-Net: learning where to look for the pancreas. MIDL. In: Proc, pp. 1\u201310 (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.03999","DOI":"10.48550\/arXiv.1804.03999"},{"issue":"10","key":"300_CR9","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2903562","journal-title":"IEEE Trans. Med. Imaging"},{"key":"300_CR10","unstructured":"Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. Adv. Neural Inf. Process. Syst. 5998\u20136008 (2017)"},{"key":"300_CR11","doi-asserted-by":"publisher","unstructured":"Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Tay, FE., Feng, J., Yan, S.: Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 558\u2013567 (2021). https:\/\/doi.org\/10.48550\/arXiv.2101.11986","DOI":"10.48550\/arXiv.2101.11986"},{"key":"300_CR12","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformer. In: European Conference on Computer Vision, pp. 213\u2013229 (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"300_CR13","doi-asserted-by":"publisher","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 36\u201346 (2021). https:\/\/doi.org\/10.48550\/arXiv.2102.10662","DOI":"10.48550\/arXiv.2102.10662"},{"key":"300_CR14","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022 (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"300_CR15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2105.05537","author":"H Cao","year":"2021","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. Arxiv Prep. (2021). https:\/\/doi.org\/10.48550\/arXiv.2105.05537","journal-title":"Arxiv Prep."},{"key":"300_CR16","doi-asserted-by":"publisher","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Wu, J.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059 (2020). https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"300_CR17","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 International Conference on Computer Vision, pp. 2018\u20132025 (2011). https:\/\/doi.org\/10.1109\/iccv.2011.6126474","DOI":"10.1109\/iccv.2011.6126474"},{"key":"300_CR18","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1117\/12.910710","volume":"8315","author":"AR Jamieson","year":"2012","unstructured":"Jamieson, A.R., Drukker, K., Giger, M.L., Van Ginneken, B., Novak, C.L.: Breast image feature learning with adaptive deconvolutional networks. Proc. SPIE Int. Soc. Opt. Eng. 8315, 64\u201376 (2012). https:\/\/doi.org\/10.1117\/12.910710","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"300_CR19","doi-asserted-by":"publisher","unstructured":"Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10076\u201310085 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01009","DOI":"10.1109\/CVPR42600.2020.01009"},{"key":"300_CR20","doi-asserted-by":"publisher","unstructured":"Petit, O., Thome, N., Rambour, C., Soler, L.: U-net transformer: self and cross attention for medical image segmentation. In: International Workshop on Machine Learning in Medical Imaging, pp. 267\u2013276 (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.06104","DOI":"10.48550\/arXiv.2103.06104"},{"issue":"11","key":"300_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-019-04285-8","volume":"32","author":"Z Tang","year":"2020","unstructured":"Tang, Z., Jiang, W., Zhang, Z., Zhao, M., Zhang, L.: DenseNet with Up-sampling block for recognizing texts in images. Neural Comput. Appl. 32(11), 1\u20139 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04285-8","journal-title":"Neural Comput. Appl."},{"issue":"3","key":"300_CR22","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1148\/radiol.2323031368","volume":"232","author":"S Bipat","year":"2004","unstructured":"Bipat, S., Glas, A.S., Slors, F.J.M., Zwinderman, A.H., Bossuyt, P.M.M., Stoker, J.: Rectal cancer: local staging and assessment of lymph node involvement with endoluminal US, CT, and MR imaging\u2014a meta-analysis. Radiology 232(3), 773\u2013783 (2004). https:\/\/doi.org\/10.1148\/radiol.2323031368","journal-title":"Radiology"},{"key":"300_CR23","doi-asserted-by":"publisher","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H.: 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). IEEE, pp. 168\u2013172 (2018). https:\/\/doi.org\/10.48550\/arXiv.1710.05006","DOI":"10.48550\/arXiv.1710.05006"},{"issue":"3","key":"300_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1944","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1944). https:\/\/doi.org\/10.2307\/1932409","journal-title":"Ecology"},{"issue":"1","key":"300_CR25","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.media.2010.08.005","volume":"15","author":"T Kubota","year":"2011","unstructured":"Kubota, T., Jerebko, A.K., Dewan, M., Salganicoff, M., Krishnan, A.: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 15(1), 133\u2013154 (2011). https:\/\/doi.org\/10.1016\/j.media.2010.08.005","journal-title":"Med. Image Anal."}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-023-00300-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13748-023-00300-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-023-00300-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:12:25Z","timestamp":1679879545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13748-023-00300-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,27]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["300"],"URL":"https:\/\/doi.org\/10.1007\/s13748-023-00300-1","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"value":"2192-6352","type":"print"},{"value":"2192-6360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,27]]},"assertion":[{"value":"15 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}