{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:37:45Z","timestamp":1791225465507,"version":"4.1.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Introduction and Cultivation Program for Young Innovative Talents of Universities in Shandong\u00a0","award":["2021QCYY003"],"award-info":[{"award-number":["2021QCYY003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11517-023-03005-8","type":"journal-article","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T04:33:20Z","timestamp":1703910800000},"page":"1201-1212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["MD-UNet: a medical image segmentation network based on mixed depthwise convolution"],"prefix":"10.1007","volume":"62","author":[{"given":"Yun","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuanglong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaole","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,30]]},"reference":[{"key":"3005_CR1","doi-asserted-by":"crossref","unstructured":"Codella NC et al (2018) 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","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"7","key":"3005_CR2","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A (2017) A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging 36(7):1550\u20131560","journal-title":"IEEE Trans Med Imaging"},{"key":"3005_CR3","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3005_CR4","doi-asserted-by":"crossref","unstructured":"Mehta S, Mercan E, Bartlett J, Weaver D, Elmore JG, Shapiro L (2018) Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16\u201320, 2018, Proceedings, Part II 11. Springer, pp 893\u2013901","DOI":"10.1007\/978-3-030-00934-2_99"},{"key":"3005_CR5","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"3005_CR6","unstructured":"Chen J et al (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"key":"3005_CR7","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"3005_CR8","doi-asserted-by":"crossref","unstructured":"Yu T, Li X, Cai Y, Sun M, Li P (2022) S2-mlp: Spatial-shift mlp architecture for vision. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 297\u2013306","DOI":"10.1109\/WACV51458.2022.00367"},{"key":"3005_CR9","unstructured":"Lian D, Yu Z, Sun X, Gao S (2021) As-mlp: An axial shifted mlp architecture for vision. arXiv preprint arXiv:2107.08391"},{"key":"3005_CR10","unstructured":"Zheng H, He P, Chen W, Zhou M (2022) Mixing and shifting: exploiting global and local dependencies in vision mlps. arXiv preprint arXiv:2202.06510"},{"issue":"4","key":"3005_CR11","doi-asserted-by":"crossref","first-page":"5314","DOI":"10.1109\/TPAMI.2022.3206148","volume":"45","author":"H Touvron","year":"2022","unstructured":"Touvron H et al (2022) Resmlp: Feedforward networks for image classification with data-efficient training. IEEE Trans Pattern Anal Mach Intell 45(4):5314\u20135321","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3005_CR12","doi-asserted-by":"crossref","unstructured":"Lv J et al (2022) CM-MLP: cascade multi-scale MLP with axial context relation encoder for edge segmentation of medical image. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp 1100\u20131107","DOI":"10.1109\/BIBM55620.2022.9995348"},{"key":"3005_CR13","unstructured":"Howard AG et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"3005_CR14","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"3005_CR15","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"3005_CR16","first-page":"288","volume":"127","author":"P Li","year":"2020","unstructured":"Li P, Wang L, Luo Y (2020) Ghost-UNet: an efficient method for wound surface segmentation. Basic Clin Pharmacol Toxicol 127:288","journal-title":"Basic Clin Pharmacol Toxicol"},{"key":"3005_CR17","doi-asserted-by":"crossref","unstructured":"Wei G, Zhang Z, Lan C, Lu Y, Chen Z (2023) Active token mixer. In: Proceedings of the AAAI Conference on Artificial Intelligence 37(3):2759\u20132767","DOI":"10.1609\/aaai.v37i3.25376"},{"issue":"6","key":"3005_CR18","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2019) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"3005_CR19","doi-asserted-by":"crossref","unstructured":"Huang H et al (2020) Unet3+: A full-scale connected Unet for medical image segmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1055\u20131059","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"3005_CR20","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.jvcir.2018.10.001","volume":"56","author":"S Lian","year":"2018","unstructured":"Lian S, Luo Z, Zhong Z, Lin X, Su S, Li S (2018) Attention guided u-net for accurate iris segmentation. J Vis Commun Image Represent 56:296\u2013304","journal-title":"J Vis Commun Image Represent"},{"key":"3005_CR21","doi-asserted-by":"crossref","unstructured":"Xiang T, Zhang C, Liu D, Song Y, Huang H, Cai W (2020) BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23. Springer, pp 74\u201384","DOI":"10.1007\/978-3-030-59710-8_8"},{"key":"3005_CR22","doi-asserted-by":"crossref","unstructured":"Ruan J, Xiang S, Xie M, Liu T, Fu Y (2022) MALUNet: a multi-attention and light-weight UNet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp 1150\u20131156","DOI":"10.1109\/BIBM55620.2022.9995040"},{"key":"3005_CR23","unstructured":"Dosovitskiy A et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"3005_CR24","doi-asserted-by":"crossref","unstructured":"Liu Z et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3005_CR25","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A et al (2022) Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"3005_CR26","doi-asserted-by":"crossref","unstructured":"Wang H, Cao P, Wang J, Zaiane, OR (2022) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence 36(3):2441\u20132449","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"3005_CR27","unstructured":"Rajagopal A, Nirmala V (2021) Convolutional gated MLP: combining convolutions & gMLP. arXiv preprint arXiv:2111.03940"},{"key":"3005_CR28","doi-asserted-by":"crossref","unstructured":"Li J, Hassani A, Walton S, Shi H (2023) Convmlp: Hierarchical convolutional mlps for vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6306\u20136315","DOI":"10.1109\/CVPRW59228.2023.00671"},{"issue":"1","key":"3005_CR29","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TPAMI.2022.3145427","volume":"45","author":"Q Hou","year":"2022","unstructured":"Hou Q, Jiang Z, Yuan L, Cheng MM, Yan S, Feng J (2022) Vision permutator: a permutable mlp-like architecture for visual recognition. IEEE Trans Pattern Anal Mach Intell 45(1):1328\u20131334","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3005_CR30","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Patel VM (2022) Unext: Mlp-based rapid medical image segmentation network. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part V. Cham: Springer Nature Switzerland, pp 23\u201333","DOI":"10.1007\/978-3-031-16443-9_3"},{"issue":"7","key":"3005_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100520","volume":"3","author":"R Liu","year":"2022","unstructured":"Liu R, Li Y, Tao L, Liang D, Zheng HT (2022) Are we ready for a new paradigm shift? a survey on visual deep mlp. Patterns 3(7):100520","journal-title":"Patterns"},{"key":"3005_CR32","doi-asserted-by":"publisher","first-page":"101864","DOI":"10.1016\/j.inffus.2023.101864","volume":"99","author":"S Guo","year":"2023","unstructured":"Guo S et al (2023) Causal knowledge fusion for 3D cross-modality cardiac image segmentation. Inf Fusion 99:101864","journal-title":"Inf Fusion"},{"key":"3005_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106084","volume":"205","author":"S Zhuang","year":"2021","unstructured":"Zhuang S, Li F, Raj ANJ, Ding W, Zhou W, Zhuang Z (2021) Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory. Comput Methods Programs Biomed 205:106084","journal-title":"Comput Methods Programs Biomed"},{"key":"3005_CR34","doi-asserted-by":"crossref","unstructured":"Zhou Z, Qi L, Yang X, Ni D, Shi Y (2022) Generalizable cross-modality medical image segmentation via style augmentation and dual normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 20856\u201320865","DOI":"10.1109\/CVPR52688.2022.02019"},{"key":"3005_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101998","volume":"111","author":"J Zhang","year":"2021","unstructured":"Zhang J et al (2021) Interactive medical image segmentation via a point-based interaction. Artif Intell Med 111:101998","journal-title":"Artif Intell Med"},{"key":"3005_CR36","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. pmlr, pp 448\u2013456"},{"issue":"6","key":"3005_CR37","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"3005_CR38","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3005_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-03005-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-03005-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-03005-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T03:01:47Z","timestamp":1730948507000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-03005-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,30]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["3005"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-03005-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,30]]},"assertion":[{"value":"1 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2023","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":"Conflict of interest"}}]}}