{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T23:58:58Z","timestamp":1793318338850,"version":"4.5.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["LZY24E050001"],"award-info":[{"award-number":["LZY24E050001"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["LZY24E060001"],"award-info":[{"award-number":["LZY24E060001"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["ZCLTGS24E0601"],"award-info":[{"award-number":["ZCLTGS24E0601"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102227"],"award-info":[{"award-number":["62102227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Major Projects of Quzhou","award":["2022K56"],"award-info":[{"award-number":["2022K56"]}]},{"name":"Science and Technology Major Projects of Quzhou","award":["2023K221"],"award-info":[{"award-number":["2023K221"]}]},{"name":"Science and Technology Major Projects of Quzhou","award":["2023K211"],"award-info":[{"award-number":["2023K211"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-93494-4","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T16:40:31Z","timestamp":1741970431000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Dual-channel compression mapping network with fused attention mechanism for medical image segmentation"],"prefix":"10.1038","volume":"15","author":[{"given":"Xiaokang","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke\u2019er","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qile","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoliang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"93494_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2024.110247","volume":"410","author":"T Saeed","year":"2024","unstructured":"Saeed, T. et al. Neuro-xai: Explainable deep learning framework based on deeplabv3+ and Bayesian optimization for segmentation and classification of brain tumor in MRI scans. J. Neurosci. Methods 410, 110247 (2024).","journal-title":"J. Neurosci. Methods"},{"key":"93494_CR2","first-page":"2861","volume":"76","author":"K Nazir","year":"2023","unstructured":"Nazir, K. et al. 3d kronecker convolutional feature pyramid for brain tumor semantic segmentation in MR imaging. Comput. Mater. Contin. 76, 2861\u20132877 (2023).","journal-title":"Comput. Mater. Contin."},{"key":"93494_CR3","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640\u2013651 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"93494_CR4","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, October 5\u20139, 2015, Proceedings, Part III 18 234\u2013241 (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"93494_CR5","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/s11760-022-02302-3","volume":"17","author":"KT Rajamani","year":"2023","unstructured":"Rajamani, K. T., Rani, P., Siebert, H., ElagiriRamalingam, R. & Heinrich, M. P. Attention-augmented u-net (aa-u-net) for semantic segmentation. SIViP 17, 981\u2013989 (2023).","journal-title":"SIViP"},{"key":"93494_CR6","first-page":"647","volume":"76","author":"WR Khan","year":"2023","unstructured":"Khan, W. R. et al. A hybrid attention-based residual Unet for semantic segmentation of brain tumor. Comput. Mater. Contin. 76, 647\u2013664 (2023).","journal-title":"Comput. Mater. Contin."},{"key":"93494_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105818","volume":"90","author":"H Yin","year":"2024","unstructured":"Yin, H. et al. Dfbu-net: Double-branch flat bottom u-net for efficient medical image segmentation. Biomed. Signal Process. Control 90, 105818 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR8","doi-asserted-by":"publisher","first-page":"14379","DOI":"10.1007\/s00521-023-08493-1","volume":"35","author":"MR Ahmed","year":"2023","unstructured":"Ahmed, M. R. et al. Doubleu-netplus: A novel attention and context-guided dual u-net with multi-scale residual feature fusion network for semantic segmentation of medical images. Neural Comput. Appl. 35, 14379\u201314401 (2023).","journal-title":"Neural Comput. Appl."},{"key":"93494_CR9","doi-asserted-by":"publisher","first-page":"14153","DOI":"10.1038\/s41598-022-16828-6","volume":"12","author":"MY Ansari","year":"2022","unstructured":"Ansari, M. Y. et al. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci. Rep. 12, 14153 (2022).","journal-title":"Sci. Rep."},{"key":"93494_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106487","volume":"96","author":"X Cao","year":"2024","unstructured":"Cao, X. et al. Demf-net: A dual encoder multi-scale feature fusion network for polyp segmentation. Biomed. Signal Process. Control 96, 106487 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR11","doi-asserted-by":"publisher","first-page":"7502504","DOI":"10.1155\/2022\/7502504","volume":"2022","author":"M Nawaz","year":"2022","unstructured":"Nawaz, M. et al. Mseg-net: A melanoma mole segmentation network using cornernet and fuzzy k-means clustering. Comput. Math. Methods Med. 2022, 7502504 (2022).","journal-title":"Comput. Math. Methods Med."},{"key":"93494_CR12","doi-asserted-by":"publisher","first-page":"1398237","DOI":"10.3389\/fbioe.2024.1398237","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun, G. et al. Da-transunet: Integrating spatial and channel dual attention with transformer u-net for medical image segmentation. Front. Bioeng. Biotechnol. 12, 1398237 (2024).","journal-title":"Front. Bioeng. Biotechnol."},{"key":"93494_CR13","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neunet.2023.11.034","volume":"170","author":"N Zhang","year":"2024","unstructured":"Zhang, N. et al. Ct-net: Asymmetric compound branch transformer for medical image segmentation. Neural Netw. 170, 298\u2013311 (2024).","journal-title":"Neural Netw."},{"key":"93494_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107728","volume":"168","author":"Y Zhao","year":"2024","unstructured":"Zhao, Y., Li, J., Ren, L. & Chen, Z. Dtan: Diffusion-based text attention network for medical image segmentation. Comput. Biol. Med. 168, 107728 (2024).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105889","volume":"90","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Luan, Z., Ni, L., Qi, L. & Gong, X. Msdanet: A multi-scale dilation attention network for medical image segmentation. Biomed. Signal Process. Control 90, 105889 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106478","volume":"153","author":"MY Ansari","year":"2023","unstructured":"Ansari, M. Y., Yang, Y., Meher, P. K. & Dakua, S. P. Dense-psp-unet: A neural network for fast inference liver ultrasound segmentation. Comput. Biol. Med. 153, 106478 (2023).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR17","doi-asserted-by":"publisher","first-page":"24528","DOI":"10.1109\/ACCESS.2022.3154771","volume":"10","author":"S Mohanty","year":"2022","unstructured":"Mohanty, S. & Dakua, S. P. Toward computing cross-modality symmetric non-rigid medical image registration. IEEE Access 10, 24528\u201324539 (2022).","journal-title":"IEEE Access"},{"key":"93494_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107770","volume":"168","author":"Z Ma","year":"2024","unstructured":"Ma, Z. & Li, X. An improved supervised and attention mechanism-based u-net algorithm for retinal vessel segmentation. Comput. Biol. Med. 168, 107770 (2024).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106285","volume":"94","author":"Y Feng","year":"2024","unstructured":"Feng, Y., Zhu, X., Zhang, X., Li, Y. & Lu, H. Pamsnet: A medical image segmentation network based on spatial pyramid and attention mechanism. Biomed. Signal Process. Control 94, 106285 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR20","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.21037\/qims-23-947","volume":"14","author":"J Chen","year":"2024","unstructured":"Chen, J. et al. Attention gate and dilation u-shaped network (gdunet): An efficient breast ultrasound image segmentation network with multiscale information extraction. Quant. Imaging Med. Surg. 14, 2034\u20132048 (2024).","journal-title":"Quant. Imaging Med. Surg."},{"key":"93494_CR21","doi-asserted-by":"publisher","first-page":"40290","DOI":"10.1109\/ACCESS.2024.3374108","volume":"12","author":"T Hussain","year":"2024","unstructured":"Hussain, T. & Shouno, H. Magres-unet: Improved medical image segmentation through a deep learning paradigm of multi-attention gated residual u-net. IEEE Access 12, 40290\u201340310 (2024).","journal-title":"IEEE Access"},{"key":"93494_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118625","volume":"211","author":"N Shen","year":"2023","unstructured":"Shen, N. et al. Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution. Expert Syst. Appl. 211, 118625 (2023).","journal-title":"Expert Syst. Appl."},{"key":"93494_CR23","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.aej.2023.02.039","volume":"70","author":"Z Fu","year":"2023","unstructured":"Fu, Z., Li, J. & Hua, Z. Msa-net: Multiscale spatial attention network for medical image segmentation. Alex. Eng. J. 70, 453\u2013473 (2023).","journal-title":"Alex. Eng. J."},{"key":"93494_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107803","volume":"168","author":"C Wang","year":"2024","unstructured":"Wang, C. et al. Cfatransunet: Channel-wise cross fusion attention and transformer for 2d medical image segmentation. Comput. Biol. Med. 168, 107803 (2024).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104515","volume":"82","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Wang, G., Wang, Z., Wang, H. & Li, Y. Triplet attention fusion module: A concise and efficient channel attention module for medical image segmentation. Biomed. Signal Process. Control 82, 104515 (2023).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105331","volume":"86","author":"X Liu","year":"2023","unstructured":"Liu, X., Hu, Y. & Chen, J. Hybrid CNN-transformer model for medical image segmentation with pyramid convolution and multi-layer perceptron. Biomed. Signal Process. Control 86, 105331 (2023).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR27","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.21037\/qims-23-1454","volume":"14","author":"L Zhang","year":"2024","unstructured":"Zhang, L., Xu, C., Li, Y., Liu, T. & Sun, J. Mcse-u-net: Multi-convolution blocks and squeeze and excitation blocks for vessel segmentation. Quant. Imaging Med. Surg. 14, 2426\u20132440 (2024).","journal-title":"Quant. Imaging Med. Surg."},{"key":"93494_CR28","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1049\/ipr2.13052","volume":"18","author":"S Jiang","year":"2024","unstructured":"Jiang, S., Chen, X. & Yi, C. Ssa-unet: Whole brain segmentation by u-net with squeeze-and-excitation block and self-attention block from the 2.5 d slice image. IET Image Process. 18, 1598\u20131612 (2024).","journal-title":"IET Image Process."},{"key":"93494_CR29","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P. & Kapur, T. Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39, 3868\u20133878 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"93494_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107437","volume":"231","author":"Y-Z Li","year":"2023","unstructured":"Li, Y.-Z. et al. Rsu-net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images. Comput. Methods Programs Biomed. 231, 107437 (2023).","journal-title":"Comput. Methods Programs Biomed."},{"key":"93494_CR31","doi-asserted-by":"publisher","first-page":"9890","DOI":"10.1109\/ACCESS.2022.3233110","volume":"11","author":"MY Ansari","year":"2022","unstructured":"Ansari, M. Y., Chandrasekar, V., Singh, A. V. & Dakua, S. P. Re-routing drugs to blood brain barrier: A comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing. IEEE Access 11, 9890\u20139906 (2022).","journal-title":"IEEE Access"},{"key":"93494_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103074","volume":"71","author":"JOB Diniz","year":"2022","unstructured":"Diniz, J. O. B., Ferreira, J. L., Diniz, P. H. B., Silva, A. C. & Paiva, A. C. A deep learning method with residual blocks for automatic spinal cord segmentation in planning CT. Biomed. Signal Process. Control 71, 103074 (2022).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108265","volume":"172","author":"X Fan","year":"2024","unstructured":"Fan, X., Zhou, J., Jiang, X., Xin, M. & Hou, L. Csap-unet: Convolution and self-attention paralleling network for medical image segmentation with edge enhancement. Comput. Biol. Med. 172, 108265 (2024).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108315","volume":"172","author":"J Li","year":"2024","unstructured":"Li, J., Gao, G., Yang, L. & Liu, Y. A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion. Comput. Biol. Med. 172, 108315 (2024).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106301","volume":"151","author":"J Li","year":"2022","unstructured":"Li, J. et al. Class-aware attention network for infectious keratitis diagnosis using corneal photographs. Comput. Biol. Med. 151, 106301 (2022).","journal-title":"Comput. Biol. Med."},{"key":"93494_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113419","volume":"153","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Feng, C. & Wang, R. Automatic segmentation model combining u-net and level set method for medical images. Expert Syst. Appl. 153, 113419 (2020).","journal-title":"Expert Syst. Appl."},{"key":"93494_CR37","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1007\/s11063-022-10953-z","volume":"55","author":"A Selvaraj","year":"2023","unstructured":"Selvaraj, A. & Nithiyaraj, E. Cedrnn: A convolutional encoder-decoder residual neural network for liver tumour segmentation. Neural Process. Lett. 55, 1605\u20131624 (2023).","journal-title":"Neural Process. Lett."},{"key":"93494_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105273","volume":"86","author":"H Abdel-Nabi","year":"2023","unstructured":"Abdel-Nabi, H., Ali, M. Z. & Awajan, A. A multi-scale 3-stacked-layer coned u-net framework for tumor segmentation in whole slide images. Biomed. Signal Process. Control 86, 105273 (2023).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR39","doi-asserted-by":"publisher","first-page":"3322993","DOI":"10.1109\/TIM.2023.3322993","volume":"72","author":"K Hu","year":"2023","unstructured":"Hu, K. et al. Dsc-net: A novel interactive two-stream network by combining transformer and CNN for ultrasound image segmentation. IEEE Trans. Instrum. Meas. 72, 3322993 (2023).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"93494_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107810","volume":"112","author":"L Wang","year":"2021","unstructured":"Wang, L. et al. Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network. Pattern Recogn. 112, 107810 (2021).","journal-title":"Pattern Recogn."},{"key":"93494_CR41","doi-asserted-by":"publisher","first-page":"3326","DOI":"10.3390\/math9243326","volume":"9","author":"N Khan","year":"2021","unstructured":"Khan, N., Haq, I. U., Ullah, F. U. M., Khan, S. U. & Lee, M. Y. Cl-net: Convlstm-based hybrid architecture for batteries\u2019 state of health and power consumption forecasting. Mathematics 9, 3326 (2021).","journal-title":"Mathematics"},{"key":"93494_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103213","volume":"71","author":"L Wang","year":"2022","unstructured":"Wang, L. et al. Ee-net: An edge-enhanced deep learning network for jointly identifying corneal micro-layers from optical coherence tomography. Biomed. Signal Process. Control 71, 103213 (2022).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR43","doi-asserted-by":"crossref","unstructured":"Karaali, A., Dahyot, R. & Sexton, D.\u00a0J. Dr-vnet: Retinal vessel segmentation via dense residual unet, in International Conference on Pattern Recognition and Artificial Intelligence 198\u2013210 (2022).","DOI":"10.1007\/978-3-031-09037-0_17"},{"key":"93494_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027","volume":"61","author":"M Byra","year":"2020","unstructured":"Byra, M. et al. Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed. Signal Process. Control 61, 102027 (2020).","journal-title":"Biomed. Signal Process. Control"},{"key":"93494_CR45","doi-asserted-by":"crossref","unstructured":"Jha, D., Riegler, M.\u00a0A., Johansen, D., Halvorsen, P. & Johansen, H.\u00a0D. Doubleu-net: A deep convolutional neural network for medical image segmentation, in 2020 IEEE 33rd International Symposium on Computer-based Medical Systems (CBMS) 558\u2013564 (2020).","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"93494_CR46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s13735-023-00270-z","volume":"12","author":"F Gouizi","year":"2023","unstructured":"Gouizi, F. & Megherbi, A. C. Nested-net: A deep nested network for background subtraction. Int. J. Multimed. Inf. Retr. 12, 5 (2023).","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"93494_CR47","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1038\/s41523-021-00358-x","volume":"7","author":"A Baccouche","year":"2021","unstructured":"Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C. & Elmaghraby, A. S. Connected-unets: A deep learning architecture for breast mass segmentation. NPJ Breast Cancer 7, 151 (2021).","journal-title":"NPJ Breast Cancer"},{"key":"93494_CR48","doi-asserted-by":"crossref","unstructured":"Bullock, J., Cuesta-L\u00e1zaro, C. & Quera-Bofarull, A. Xnet: A convolutional neural network (CNN) implementation for medical X-ray image segmentation suitable for small datasets, in Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 10953, 453\u2013463 (2019).","DOI":"10.1117\/12.2512451"},{"key":"93494_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, Q.-L. & Yang, Y.-B. Sa-net: Shuffle attention for deep convolutional neural networks, in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2235\u20132239 (2021).","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"93494_CR50","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jocs.2018.05.002","volume":"27","author":"X Zhai","year":"2018","unstructured":"Zhai, X. et al. Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip. J. Comput. Sci. 27, 35\u201345 (2018).","journal-title":"J. Comput. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93494-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93494-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93494-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T17:09:45Z","timestamp":1741972185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93494-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["93494"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-93494-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]},"assertion":[{"value":"4 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8906"}}