{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T15:16:16Z","timestamp":1726326976295},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"am","delay-in-days":669,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19H04215","19K20345"],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1016\/j.neucom.2023.126970","type":"journal-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T04:33:43Z","timestamp":1698554023000},"page":"126970","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["A multi-attention and depthwise separable convolution network for medical image segmentation"],"prefix":"10.1016","volume":"564","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7538-7754","authenticated-orcid":false,"given":"Yuxiang","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6024-3598","authenticated-orcid":false,"given":"Xin","family":"Kang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4860-9184","authenticated-orcid":false,"given":"Fuji","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9794-3221","authenticated-orcid":false,"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6293-0428","authenticated-orcid":false,"given":"Satoshi","family":"Nakagawa","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8140-8512","authenticated-orcid":false,"given":"Xiao","family":"Shan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2023.126970_b1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2021.08.157","article-title":"Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey","volume":"490","author":"Altini","year":"2022","journal-title":"Neurocomputing"},{"issue":"8","key":"10.1016\/j.neucom.2023.126970_b2","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic covid-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2023.126970_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105810","article-title":"Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation","volume":"148","author":"Qi","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.neucom.2023.126970_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.11","article-title":"A large, open source dataset of stroke anatomical brain images and manual lesion segmentations","volume":"5","author":"Liew","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.neucom.2023.126970_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101950","article-title":"CHAOS challenge-combined (CT-mr) healthy abdominal organ segmentation","volume":"69","author":"Kavur","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2023.126970_b6","series-title":"Ultrasound nerve segmentation kaggle","year":"2016"},{"key":"10.1016\/j.neucom.2023.126970_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105966","article-title":"Background selection schema on deep learning-based classification of dermatological disease","volume":"149","author":"Zhou","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"10.1016\/j.neucom.2023.126970_b8","doi-asserted-by":"crossref","first-page":"3231","DOI":"10.1109\/TMI.2022.3180435","article-title":"Benchmarking of deep architectures for segmentation of medical images","volume":"41","author":"Gut","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2023.126970_b9","doi-asserted-by":"crossref","unstructured":"A. Rahman, J.M.J. Valanarasu, I. Hacihaliloglu, V.M. Patel, Ambiguous medical image segmentation using diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11536\u201311546.","DOI":"10.1109\/CVPR52729.2023.01110"},{"issue":"6","key":"10.1016\/j.neucom.2023.126970_b10","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1007\/s42235-022-00234-9","article-title":"Osteoporotic vertebral fracture classification in X-rays based on a multi-modal semantic consistency network","volume":"19","author":"Wang","year":"2022","journal-title":"J. Bionic Eng."},{"key":"10.1016\/j.neucom.2023.126970_b11","article-title":"Causality-inspired single-source domain generalization for medical image segmentation","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2023.126970_b12","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neucom.2023.126970_b13","first-page":"1","article-title":"Ds-transunet: Dual swin transformer u-net for medical image segmentation","volume":"71","author":"Lin","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.neucom.2023.126970_b14","doi-asserted-by":"crossref","first-page":"47419","DOI":"10.1109\/ACCESS.2020.2977946","article-title":"A partitioning-stacking prediction fusion network based on an improved attention U-Net for stroke lesion segmentation","volume":"8","author":"Hui","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2023.126970_b15","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24","first-page":"61","article-title":"UTNet: a hybrid transformer architecture for medical image segmentation","author":"Gao","year":"2021"},{"key":"10.1016\/j.neucom.2023.126970_b16","doi-asserted-by":"crossref","unstructured":"F. Chollet, Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.neucom.2023.126970_b17","series-title":"Advances in Neural Information Processing Systems","article-title":"Attention is all you need","volume":"Vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2023.126970_b18","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2023.126970_b19","doi-asserted-by":"crossref","unstructured":"Y. Zhou, X. Kang, F. Ren, MDSU-Net: A Multi-attention and Depthwise Separable Convolution Network for Stroke Lesion Segmentation, in: Proceedings of the 2022 9th International Conference on Biomedical and Bioinformatics Engineering, 2022, pp. 11\u201316.","DOI":"10.1145\/3574198.3574200"},{"key":"10.1016\/j.neucom.2023.126970_b20","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24","first-page":"36","article-title":"Medical transformer: Gated axial-attention for medical image segmentation","author":"Valanarasu","year":"2021"},{"key":"10.1016\/j.neucom.2023.126970_b21","doi-asserted-by":"crossref","unstructured":"W. Ji, S. Yu, J. Wu, K. Ma, C. Bian, Q. Bi, J. Li, H. Liu, L. Cheng, Y. Zheng, Learning calibrated medical image segmentation via multi-rater agreement modeling, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 12341\u201312351.","DOI":"10.1109\/CVPR46437.2021.01216"},{"key":"10.1016\/j.neucom.2023.126970_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2023.104742","article-title":"Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation","author":"Huang","year":"2023","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.neucom.2023.126970_b23","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"247","article-title":"X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies","author":"Qi","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b24","doi-asserted-by":"crossref","first-page":"178486","DOI":"10.1109\/ACCESS.2019.2958384","article-title":"MSDF-net: Multi-scale deep fusion network for stroke lesion segmentation","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2023.126970_b25","series-title":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"1150","article-title":"MALUNet: A multi-attention and light-weight unet for skin lesion segmentation","author":"Ruan","year":"2022"},{"key":"10.1016\/j.neucom.2023.126970_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106952","article-title":"Rema-Net: An efficient multi-attention convolutional neural network for rapid skin lesion segmentation","volume":"159","author":"Yang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2023.126970_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104836","article-title":"TA-Net: Triple attention network for medical image segmentation","volume":"137","author":"Li","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2023.126970_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106428","article-title":"MAD-Net: Multi-attention dense network for functional bone marrow segmentation","volume":"154","author":"Qin","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2023.126970_b29","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.neucom.2023.126970_b30","series-title":"European Conference on Computer Vision","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.neucom.2023.126970_b31","doi-asserted-by":"crossref","unstructured":"A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H.R. Roth, D. Xu, Unetr: Transformers for 3d medical image segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.neucom.2023.126970_b32","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24","first-page":"171","article-title":"Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation","author":"Xie","year":"2021"},{"issue":"3","key":"10.1016\/j.neucom.2023.126970_b33","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.neucom.2023.126970_b34","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.neuroimage.2017.04.039","article-title":"3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study","volume":"170","author":"Dolz","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.neucom.2023.126970_b35","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"Ieee Access"},{"key":"10.1016\/j.neucom.2023.126970_b36","doi-asserted-by":"crossref","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical image segmentation based on u-net: A review","author":"Du","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"10.1016\/j.neucom.2023.126970_b37","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22","first-page":"523","article-title":"Learning shape priors for robust cardiac MR segmentation from multi-view images","author":"Chen","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b38","series-title":"Medical Imaging 2020: Image Processing","first-page":"282","article-title":"Unified multi-scale feature abstraction for medical image segmentation","volume":"Vol. 11313","author":"Fang","year":"2020"},{"key":"10.1016\/j.neucom.2023.126970_b39","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22","first-page":"759","article-title":"HFA-Net: 3D cardiovascular image segmentation with asymmetrical pooling and content-aware fusion","author":"Zheng","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b40","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"184","article-title":"3D dilated multi-fiber network for real-time brain tumor segmentation in MRI","author":"Chen","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b41","doi-asserted-by":"crossref","unstructured":"X. Yan, H. Tang, S. Sun, H. Ma, D. Kong, X. Xie, After-unet: Axial fusion transformer unet for medical image segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 3971\u20133981.","DOI":"10.1109\/WACV51458.2022.00333"},{"key":"10.1016\/j.neucom.2023.126970_b42","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"455","article-title":"Focusnet: An attention-based fully convolutional network for medical image segmentation","author":"Kaul","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b43","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"issue":"1","key":"10.1016\/j.neucom.2023.126970_b44","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","article-title":"Multi-scale self-guided attention for medical image segmentation","volume":"25","author":"Sinha","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2023.126970_b45","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6","first-page":"327","article-title":"Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution","author":"Henry","year":"2021"},{"key":"10.1016\/j.neucom.2023.126970_b46","series-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.neucom.2023.126970_b47","doi-asserted-by":"crossref","unstructured":"A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, et al., Searching for mobilenetv3, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10.1016\/j.neucom.2023.126970_b48","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.neucom.2023.126970_b49","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.neucom.2023.126970_b50","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2023.126970_b51","series-title":"2016 Fourth International Conference on 3D Vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.neucom.2023.126970_b52","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.neucom.2023.126970_b53","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"266","article-title":"CLCI-Net: Cross-level fusion and context inference networks for lesion segmentation of chronic stroke","author":"Yang","year":"2019"},{"key":"10.1016\/j.neucom.2023.126970_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106717","article-title":"SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization","volume":"156","author":"Yu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2023.126970_b55","article-title":"Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network","volume":"27","author":"Tomita","year":"2020","journal-title":"NeuroImage: Clinical"},{"key":"10.1016\/j.neucom.2023.126970_b56","doi-asserted-by":"crossref","unstructured":"S. J\u00e9gou, M. Drozdzal, D. Vazquez, A. Romero, Y. Bengio, The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 11\u201319.","DOI":"10.1109\/CVPRW.2017.156"},{"issue":"3","key":"10.1016\/j.neucom.2023.126970_b57","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/JBHI.2023.3238183","article-title":"Sgu-net: Shape-guided ultralight network for abdominal image segmentation","volume":"27","author":"Lei","year":"2023","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.neucom.2023.126970_b58","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"Nas-unet: Neural architecture search for medical image segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223010937?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223010937?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T20:43:14Z","timestamp":1700426594000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231223010937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":58,"alternative-id":["S0925231223010937"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126970","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-attention and depthwise separable convolution network for medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126970","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"126970"}}