{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:48:17Z","timestamp":1740185297145,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020ZDPY0303"],"award-info":[{"award-number":["2020ZDPY0303"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Program of National Natural Science Foundation of China","award":["61976218"],"award-info":[{"award-number":["61976218"]}]},{"name":"Key R & D projects in Xuzhou","award":["KC22111","KC22095"],"award-info":[{"award-number":["KC22111","KC22095"]}]},{"name":"Xuzhou Medical Leading Talents Training Project","award":["XWKYHT20220073","XWRCHT20210025"],"award-info":[{"award-number":["XWKYHT20220073","XWRCHT20210025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01162-2","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T17:00:03Z","timestamp":1719594003000},"page":"2983-2995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images"],"prefix":"10.1007","volume":"37","author":[{"given":"Zhiyang","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2688-7477","authenticated-orcid":false,"given":"Yanzi","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Guangxia","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shiyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hu","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"issue":"4","key":"1162_CR1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1159\/000518232","volume":"40","author":"M Tokat","year":"2022","unstructured":"Tokat M, van Tilburg L, Koch AD, Spaander MC (2022) Artificial intelligence in upper gastrointestinal endoscopy. Dig Dis 40(4):395\u2013408. https:\/\/doi.org\/10.1159\/000518232","journal-title":"Dig Dis"},{"issue":"2","key":"1162_CR2","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1111\/jgh.16059","volume":"38","author":"S Shah","year":"2023","unstructured":"Shah S, Park N, Chehade NEH, Chahine A, Monachese M, Tiritilli A, Samarasena J (2023) Effect of computer-aided colonoscopy on adenoma miss rates and polyp detection: a systematic review and meta-analysis. J Gastroenterol Hepatol 38(2):162\u2013176. https:\/\/doi.org\/10.1111\/jgh.16059","journal-title":"J Gastroenterol Hepatol"},{"issue":"1","key":"1162_CR3","doi-asserted-by":"publisher","first-page":"124","DOI":"10.4251\/wjgo.v14.i1.124","volume":"14","author":"F Liang","year":"2022","unstructured":"Liang F, Wang S, Zhang K, Liu TJ, Li JN (2022) Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World Journal of Gastrointestinal Oncology 14(1):124. https:\/\/doi.org\/10.4251\/wjgo.v14.i1.124","journal-title":"World Journal of Gastrointestinal Oncology"},{"doi-asserted-by":"publisher","unstructured":"Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: a nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 3\u201311). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","key":"1162_CR4","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"6","key":"1162_CR5","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1109\/JBHI.2021.3049304","volume":"25","author":"D Jha","year":"2021","unstructured":"Jha D, Smedsrud PH, Johansen D, de Lange T, Johansen HD, Halvorsen P, Riegler MA (2021) A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation. IEEE J Biomed Health Inform 25(6):2029\u20132040. https:\/\/doi.org\/10.1109\/JBHI.2021.3049304","journal-title":"IEEE J Biomed Health Inform"},{"doi-asserted-by":"publisher","unstructured":"Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019, December). Resunet++: an advanced architecture for medical image segmentation. In 2019 IEEE international symposium on multimedia (ISM) (pp. 225\u20132255). IEEE. https:\/\/doi.org\/10.1109\/ISM46123.2019.00049","key":"1162_CR6","DOI":"10.1109\/ISM46123.2019.00049"},{"doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). 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 (pp. 234\u2013241). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","key":"1162_CR7","DOI":"10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"publisher","unstructured":"Fan, D. P., Ji, G. P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020, September). Pranet: parallel reverse attention network for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 263\u2013273). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-59725-2_26","key":"1162_CR8","DOI":"10.1007\/978-3-030-59725-2_26"},{"doi-asserted-by":"publisher","unstructured":"Wu, Z., Su, L., & Huang, Q. (2019). Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 3907\u20133916) https:\/\/doi.org\/10.1109\/CVPR.2019.00403","key":"1162_CR9","DOI":"10.1109\/CVPR.2019.00403"},{"doi-asserted-by":"publisher","unstructured":"Kim, T., Lee, H., & Kim, D. (2021, October). Uacanet: uncertainty augmented context attention for polyp segmentation. In Proceedings of the 29th ACM international conference on multimedia (pp. 2167\u20132175). https:\/\/doi.org\/10.1145\/3474085.3475375","key":"1162_CR10","DOI":"10.1145\/3474085.3475375"},{"issue":"1","key":"1162_CR11","doi-asserted-by":"publisher","first-page":"014005","DOI":"10.1117\/1.JMI.10.1.014005","volume":"10","author":"A Lou","year":"2023","unstructured":"Lou A, Guan S, Loew M (2023) Caranet: context axial reverse attention network for segmentation of small medical objects. Journal of Medical Imaging 10(1):014005\u2013014005. https:\/\/doi.org\/10.1117\/1.JMI.10.1.014005","journal-title":"Journal of Medical Imaging"},{"key":"1162_CR12","doi-asserted-by":"publisher","first-page":"80575","DOI":"10.1109\/ACCESS.2022.3195241","volume":"10","author":"NT Duc","year":"2022","unstructured":"Duc NT, Oanh NT, Thuy NT, Triet TM, Dinh VS (2022) Colonformer: an efficient transformer based method for colon polyp segmentation. IEEE Access 10:80575\u201380586. https:\/\/doi.org\/10.1109\/ACCESS.2022.3195241","journal-title":"IEEE Access"},{"key":"1162_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106274","volume":"151","author":"C Wu","year":"2022","unstructured":"Wu C, Long C, Li S, Yang J, Jiang F, Zhou R (2022) MSRAformer: multiscale spatial reverse attention network for polyp segmentation. Comput Biol Med 151. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106274","journal-title":"Comput Biol Med"},{"key":"1162_CR14","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Proces Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Proces Syst"},{"doi-asserted-by":"publisher","unstructured":"Zhang, Y., Liu, H., & Hu, Q. (2021). Transfuse: fusing transformers and cnns for medical image segmentation. In Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th International conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24 (pp. 14\u201324). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-87193-2_2","key":"1162_CR15","DOI":"10.1007\/978-3-030-87193-2_2"},{"doi-asserted-by":"publisher","unstructured":"Sanderson, E., & Matuszewski, B. J. (2022, July). FCN-transformer feature fusion for polyp segmentation. In Annual conference on medical image understanding and analysis (pp. 892\u2013907). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-031-12053-4_65","key":"1162_CR16","DOI":"10.1007\/978-3-031-12053-4_65"},{"doi-asserted-by":"publisher","unstructured":"Dong, B., Wang, W., Fan, D. P., Li, J., Fu, H., & Shao, L. (2021). Polyp-pvt: polyp segmentation with pyramid vision transformers. arXiv preprint arXiv:2108.06932. https:\/\/doi.org\/10.48550\/arXiv.2108.06932","key":"1162_CR17","DOI":"10.48550\/arXiv.2108.06932"},{"doi-asserted-by":"publisher","unstructured":"Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., & Song, S. (2022, September). Stepwise feature fusion: local guides global. In International conference on medical image computing and computer-assisted intervention (pp. 110\u2013120). Cham: Springer Nature Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-16437-8_11","key":"1162_CR18","DOI":"10.1007\/978-3-031-16437-8_11"},{"doi-asserted-by":"publisher","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 6881\u20136890). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00681","key":"1162_CR19","DOI":"10.1109\/CVPR46437.2021.00681"},{"doi-asserted-by":"publisher","unstructured":"Srivastava, A., Chanda, S., Jha, D., Pal, U., & Ali, S. (2022, August). GMSRF-Net: an improved generalizability with global multi-scale residual fusion network for polyp segmentation. In 2022 26th International Conference on Pattern Recognition (ICPR) (pp. 4321\u20134327). IEEE. https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956726","key":"1162_CR20","DOI":"10.1109\/ICPR56361.2022.9956726"},{"doi-asserted-by":"publisher","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., & Zhou, Y. (2021). Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. https:\/\/doi.org\/10.48550\/arXiv.2102.04306","key":"1162_CR21","DOI":"10.48550\/arXiv.2102.04306"},{"doi-asserted-by":"publisher","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2022, October). Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision (pp. 205\u2013218). Cham: Springer Nature Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","key":"1162_CR22","DOI":"10.1007\/978-3-031-25066-8_9"},{"doi-asserted-by":"publisher","unstructured":"Sang, D. V., Chung, T. Q., Lan, P. N., Hang, D. V., Van Long, D., & Thuy, N. T. (2021). Ag-curesnest: a novel method for colon polyp segmentation. arXiv preprint arXiv:2105.00402. https:\/\/doi.org\/10.48550\/arXiv.2105.00402","key":"1162_CR23","DOI":"10.48550\/arXiv.2105.00402"},{"doi-asserted-by":"publisher","unstructured":"Cai, L., Wu, M., Chen, L., Bai, W., Yang, M., Lyu, S., & Zhao, Q. (2022, September). Using guided self-attention with local information for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 629\u2013638). Cham: Springer Nature Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-16440-8_60","key":"1162_CR24","DOI":"10.1007\/978-3-031-16440-8_60"},{"key":"1162_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104304","volume":"139","author":"H Zhang","year":"2023","unstructured":"Zhang H, Yang X, Li D, Cui Y, Zhao J, Qiu S (2023) Dual parallel net: a novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior. J Biomed Inform 139. https:\/\/doi.org\/10.1016\/j.jbi.2023.104304","journal-title":"J Biomed Inform"},{"issue":"3","key":"1162_CR26","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, Fan DP, Song K, Liang D, Shao L (2022) Pvt v2: improved baselines with pyramid vision transformer. Computational Visual Media 8(3):415\u2013424. https:\/\/doi.org\/10.1007\/s41095-022-0274-8","journal-title":"Computational Visual Media"},{"doi-asserted-by":"publisher","unstructured":"Wei, J., Wang, S., & Huang, Q. (2020, April). F3Net: fusion, feedback and focus for salient object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 12321\u201312328). https:\/\/doi.org\/10.1609\/aaai.v34i07.6916","key":"1162_CR27","DOI":"10.1609\/aaai.v34i07.6916"},{"doi-asserted-by":"publisher","unstructured":"Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. D. (2020). Kvasir-seg: a segmented polyp dataset. In MultiMedia modeling: 26th international conference, MMM 2020, Daejeon, South Korea, January 5\u20138, 2020, Proceedings, Part II 26 (pp. 451\u2013462). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-37734-2_37","key":"1162_CR28","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"1162_CR29","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111. https:\/\/doi.org\/10.1016\/j.compmedimag.2015.02.007","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"1162_CR30","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","volume":"35","author":"N Tajbakhsh","year":"2015","unstructured":"Tajbakhsh N, Gurudu SR, Liang J (2015) Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 35(2):630\u2013644. https:\/\/doi.org\/10.1109\/TMI.2015.2487997","journal-title":"IEEE Trans Med Imaging"},{"key":"1162_CR31","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9:283\u2013293. https:\/\/doi.org\/10.1007\/s11548-013-0926-3","journal-title":"Int J Comput Assist Radiol Surg"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01162-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01162-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01162-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T19:09:11Z","timestamp":1733166551000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01162-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1162"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01162-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"7 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","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 experimental cases in this study were obtained from The First People\u2019s Hospital of Xuzhou. Subjects\u2019 informed consent was obtained for any form of biomedical, clinical, and biometric personally identifiable data included in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}