{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:59:02Z","timestamp":1774594742860,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["82261138629"],"award-info":[{"award-number":["82261138629"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation under Grant","award":["2023A1515010688"],"award-info":[{"award-number":["2023A1515010688"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation under Grant","award":["2021A1515220072"],"award-info":[{"award-number":["2021A1515220072"]}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["JCYJ20220531101412030"],"award-info":[{"award-number":["JCYJ20220531101412030"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["JCYJ20220530155811025"],"award-info":[{"award-number":["JCYJ20220530155811025"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10884-x","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T10:27:06Z","timestamp":1735036026000},"page":"4651-4661","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation"],"prefix":"10.1007","volume":"37","author":[{"given":"Yuxuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuechen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfei","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1420-0815","authenticated-orcid":false,"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"10884_CR1","unstructured":"of the People N H C. Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer (2023) edition)[J]. Zhonghua wai ke za zhi [Chinese journal of surgery] 61(8):617\u2013644"},{"key":"10884_CR2","doi-asserted-by":"crossref","unstructured":"Wang Y, Li X, Liu J, et al. (2021) A contrastive learning-based ppc-unet for colorectal histopathology whole slide image segmentation. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, : 2072-2079","DOI":"10.1109\/BIBM52615.2021.9669780"},{"key":"10884_CR3","doi-asserted-by":"crossref","unstructured":"Chen H, Qi X, Yu L, et al. (2016) DCAN: deep contour-aware networks for accurate gland segmentation. In: proceedings of the ieee conference on computer vision and pattern recognition. p 2487-2496","DOI":"10.1109\/CVPR.2016.273"},{"key":"10884_CR4","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"10884_CR5","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. Advances in neural information processing systems"},{"key":"10884_CR6","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"10884_CR7","first-page":"15908","volume":"34","author":"K Han","year":"2021","unstructured":"Han K, Xiao A, Wu E et al (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908\u201315919","journal-title":"Adv Neural Inf Process Syst"},{"key":"10884_CR8","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, et al. (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision: 10012-10022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10884_CR9","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, et al. (2021) Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF international conference on computer vision: 568-578","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10884_CR10","unstructured":"Chu X, Tian Z, Wang Y, et al. (2021) Twins: Revisiting spatial attention design in vision transformers. arXiv preprint arXiv:2104.13840"},{"key":"10884_CR11","unstructured":"Chu X, Tian Z, Zhang B, et al. (2021) Conditional positional encodings for vision transformers. arXiv preprint arXiv:2102.10882"},{"key":"10884_CR12","doi-asserted-by":"crossref","unstructured":"Chen C F R, Fan Q, Panda R. (2021) Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision: 357-366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"10884_CR13","unstructured":"Li Y, Zhang K, Cao J, et al. (2021) Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707"},{"issue":"3","key":"10884_CR14","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang W, Xie E, Li X et al (2022) Pvt v2: improved baselines with pyramid vision transformer. Comput Visual Media 8(3):415\u2013424","journal-title":"Comput Visual Media"},{"key":"10884_CR15","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin IO, Houlsby N, Kolesnikov A et al (2021) Mlp-mixer: an all-mlp architecture for vision. Adv Neural Inf Process Syst 34:24261\u201324272","journal-title":"Adv Neural Inf Process Syst"},{"key":"10884_CR16","unstructured":"Yan H, Zhang C, Wu M. Lawin transformer: (2022) Improving semantic segmentation transformer with multi-scale representations via large window attention. arXiv preprint arXiv:2201.01615"},{"key":"10884_CR17","doi-asserted-by":"crossref","unstructured":"Li G, Xu D, Cheng X, et al. (2022) Simvit: Exploring a simple vision transformer with sliding windows. In: 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, : 1-6","DOI":"10.1109\/ICME52920.2022.9859907"},{"key":"10884_CR18","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana K, Pluim JPW, Chen H et al (2017) Gland segmentation in colon histology images: the glas challenge contest. Med Image Anal 35:489\u2013502","journal-title":"Med Image Anal"},{"key":"10884_CR19","doi-asserted-by":"crossref","unstructured":"Cheikh BB, Bertheau P, Racoceanu D (2016) A structure-based approach for colon gland segmentation in digital pathology. In: Medical Imaging 2016: Digital Pathology. SPIE 9791:151\u2013158","DOI":"10.1117\/12.2216545"},{"key":"10884_CR20","doi-asserted-by":"crossref","unstructured":"Chen H, Qi X, Yu L, et al. (2016) DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition: 2487-2496","DOI":"10.1109\/CVPR.2016.273"},{"key":"10884_CR21","doi-asserted-by":"crossref","unstructured":"Wang Y, Li X, Liu J, et al. (2021) A Contrastive Learning-based PPC-UNet for Colorectal Histopathology Whole Slide Image Segmentation. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, : 2072-2079","DOI":"10.1109\/BIBM52615.2021.9669780"},{"key":"10884_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929,"},{"key":"10884_CR23","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z et al (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"10884_CR24","doi-asserted-by":"crossref","unstructured":"Li G, Xu D, Cheng X, et al. (2022) Simvit: Exploring a simple vision transformer with sliding windows. In: 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE: 1-6","DOI":"10.1109\/ICME52920.2022.9859907"},{"key":"10884_CR25","doi-asserted-by":"crossref","unstructured":"Choe J, Park C, Rameau F, et al. (2022) Pointmixer: Mlp-mixer for point cloud understanding. In: European Conference on Computer Vision. Cham: Springer Nature Switzerland: 620-640","DOI":"10.1007\/978-3-031-19812-0_36"},{"key":"10884_CR26","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S A. V-net: (2016) Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). Ieee: 565-571","DOI":"10.1109\/3DV.2016.79"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10884-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10884-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10884-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T19:35:19Z","timestamp":1739043319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10884-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":26,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10884"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10884-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]},"assertion":[{"value":"23 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}