{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T07:14:21Z","timestamp":1792826061930,"version":"4.5.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"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":["SIViP"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s11760-026-05312-7","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:43:19Z","timestamp":1776217399000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Revisiting the ordering of channel and spatial attention: a comprehensive study on sequential and parallel designs"],"prefix":"10.1007","volume":"20","author":[{"given":"Zhongming","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingbing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"5312_CR1","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"5312_CR2","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"5312_CR3","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5312_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.131369","volume":"655","author":"Z Liu","year":"2025","unstructured":"Liu, Z., Liu, X., Qu, L., Shi, Y.: Fancl: Feature-guided attention network with curriculum learning for brain metastases segmentation. Neurocomputing 655, 131369 (2025)","journal-title":"Neurocomputing"},{"key":"5312_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107636","volume":"110","author":"M Liu","year":"2021","unstructured":"Liu, M., Fang, W., Ma, X., Xu, W., Xiong, N., Ding, Y.: Channel pruning guided by spatial and channel attention for dnns in intelligent edge computing. Appl. Soft Comput. 110, 107636 (2021)","journal-title":"Appl. Soft Comput."},{"key":"5312_CR6","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5312_CR7","unstructured":"Park, J., Woo, S., Lee, J.-Y., Kweon, I.-S.: Bam: Bottleneck attention module. In: British Machine Vision Conference (2018)"},{"key":"5312_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2025.110288","volume":"239","author":"Y Lu","year":"2026","unstructured":"Lu, Y., Xu, C., Gong, J., Gao, Q., Sun, D., Zhu, D.: Cm-csamfnet: A cross-modality channel and spatial attention module fusion network for multimodal medical image fusion. Signal Process. 239, 110288 (2026)","journal-title":"Signal Process."},{"key":"5312_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108243","volume":"112","author":"J Li","year":"2026","unstructured":"Li, J., Zhang, F., Zhang, G., Yang, Z., Wei, H.: Robust medical image zero watermarking algorithm based on spatial and channel synergistic attention and focalnet. Biomed. Signal Process. Control 112, 108243 (2026)","journal-title":"Biomed. Signal Process. Control"},{"key":"5312_CR10","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 0\u20130 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"5312_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"5312_CR12","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, F., Fan, X., Huang, D.: Polarized self-attention: Towards high-quality pixel-wise regression. arXiv preprint arXiv:2107.00782 (2021)","DOI":"10.1016\/j.neucom.2022.07.054"},{"key":"5312_CR13","unstructured":"Li, X., Hu, X., Yang, J.: Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. arXiv preprint arXiv:1905.09646 (2019)"},{"key":"5312_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107341","volume":"102","author":"Y Yi","year":"2025","unstructured":"Yi, Y., Wu, X., He, Y., Wu, H., Zhou, B., Luo, S., Dai, J., Du, Y., Zhou, W.: Windowed axial shuffle attention networks for medical image segmentation. Biomed. Signal Process. Control 102, 107341 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"5312_CR15","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.-P., Qiao, Z.-X., Li, S.-F., Duan, W.-S., Lei, G.-L.: Irregularly seismic data interpolation based on deep learning with integrated channel-spatial attention mechanism. Petroleum Science (2025)","DOI":"10.1016\/j.petsci.2025.10.004"},{"key":"5312_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108807","volume":"113","author":"G Peng","year":"2026","unstructured":"Peng, G., Lu, X., Chen, Y., Chen, H., Zhai, Z., Chen, T., Xie, Q.: Scea-net: A hybrid framework from spatial-channel-aware external attention for accurate 3d medical image segmentation. Biomed. Signal Process. Control 113, 108807 (2026)","journal-title":"Biomed. Signal Process. Control"},{"key":"5312_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111958","volume":"160","author":"C Chen","year":"2025","unstructured":"Chen, C., Liu, Y., Zhang, C., Li, J., Chen, X.: An efficient multi-task forest fire and smoke detection model. Eng. Appl. Artif. Intell. 160, 111958 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5312_CR18","doi-asserted-by":"crossref","unstructured":"Peng, Y., Li, H., Wang, M., Qin, L., Du, Y., Yi, Y.: Pmsfinet: Progressive multi-scale feature interaction network for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 1\u201314 (2025)","DOI":"10.1109\/JBHI.2025.3638590"},{"key":"5312_CR19","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Munir, M., Marculescu, R.: Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11769\u201311779 (2024)","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"5312_CR20","doi-asserted-by":"crossref","unstructured":"Wazir, S., Kim, D.: Rethinking decoder design: Improving biomarker segmentation using depth-to-space restoration and residual linear attention. In: 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 30861\u201330871 (2025)","DOI":"10.1109\/CVPR52734.2025.02874"},{"key":"5312_CR21","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"5312_CR22","doi-asserted-by":"crossref","unstructured":"Ma, J., Jiang, W., Tang, X., Zhang, X., Liu, F., Jiao, L.: Multiscale sparse cross-attention network for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing (2025)","DOI":"10.1109\/TGRS.2025.3525582"},{"issue":"5","key":"5312_CR23","doi-asserted-by":"publisher","first-page":"0303670","DOI":"10.1371\/journal.pone.0303670","volume":"19","author":"P Pramanik","year":"2024","unstructured":"Pramanik, P., Roy, A., Cuevas, E., Perez-Cisneros, M., Sarkar, R.: Dau-net: Dual attention-aided u-net for segmenting tumor in breast ultrasound images. PLoS ONE 19(5), 0303670 (2024)","journal-title":"PLoS ONE"},{"key":"5312_CR24","doi-asserted-by":"crossref","unstructured":"Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z., Hao, H., Qi, H., Zheng, Y., Frangi, A., Liu, J.: Cs-net: Channel and spatial attention network for curvilinear structure segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 721\u2013730 (2019). Springer","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"5312_CR25","doi-asserted-by":"crossref","unstructured":"Chen, S., Ding, C., Li, M., Lyu, X., Li, X., Xu, Z., Fang, Y., Li, H.: Pafnet: A parallel attention fusion network for water body extraction of remote sensing images. Remote Sensing 18(1) (2026)","DOI":"10.3390\/rs18010153"},{"key":"5312_CR26","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-981-96-6972-1_28","volume-title":"Neural Information Processing","author":"R-Y Ju","year":"2025","unstructured":"Ju, R.-Y., Chien, C.-T., Chiang, J.-S.: Yolov8-rescbam: Yolov8 based on an effective attention module for pediatric wrist fracture detection. In: Mahmud, M., Doborjeh, M., Wong, K., Leung, A.C.S., Doborjeh, Z., Tanveer, M. (eds.) Neural Information Processing, pp. 403\u2013416. Springer, Singapore (2025)"},{"key":"5312_CR27","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)"},{"issue":"1","key":"5312_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., Ni, B.: Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data 10(1), 41 (2023)","journal-title":"Scientific Data"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05312-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05312-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05312-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T18:39:41Z","timestamp":1778870381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05312-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":28,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["5312"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05312-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]},"assertion":[{"value":"12 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2026","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"261"}}