{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:31:32Z","timestamp":1773797492685,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Provincial Natural Science Foundation of Anhui","award":["1908085MF217"],"award-info":[{"award-number":["1908085MF217"]}]},{"name":"Natural Science Research Project of Anhui Provincial Education Department","award":["KJ2019A0022918005"],"award-info":[{"award-number":["KJ2019A0022918005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276146"],"award-info":[{"award-number":["62276146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s13042-023-01950-2","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T12:02:26Z","timestamp":1695211346000},"page":"963-983","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Segmentation-based context-aware enhancement network for medical images"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3092-7670","authenticated-orcid":false,"given":"Hua","family":"Bao","sequence":"first","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuqing","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"1950_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101903","volume":"59","author":"B Richhariya","year":"2020","unstructured":"Richhariya B, Tanveer M, Rashid AH, Initiative ADN et al (2020) Diagnosis of Alzheimer\u2019s disease using Universum support vector machine based recursive feature elimination (usvm-rfe). Biomed Sig Proc Control 59:101903","journal-title":"Biomed Sig Proc Control"},{"issue":"4","key":"1950_CR2","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1109\/JBHI.2021.3083274","volume":"26","author":"M Tanveer","year":"2021","unstructured":"Tanveer M, Rashid AH, Ganaie M, Reza M, Razzak I, Hua K-L (2021) Classification of Alzheimer\u2019s disease using ensemble of deep neural networks trained through transfer learning. IEEE J Biomed Health Inform 26(4):1453\u20131463","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"1950_CR3","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1109\/JBHI.2021.3083187","volume":"26","author":"I Beheshti","year":"2021","unstructured":"Beheshti I, Ganaie M, Paliwal V, Rastogi A, Razzak I, Tanveer M (2021) Predicting brain age using machine learning algorithms: A comprehensive evaluation. IEEE J Biomed Health Inf 26(4):1432\u20131440","journal-title":"IEEE J Biomed Health Inf"},{"issue":"2","key":"1950_CR4","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","volume":"41","author":"Z Ning","year":"2021","unstructured":"Ning Z, Zhong S, Feng Q, Chen W, Zhang Y (2021) Smu-net: saliency-guided morphology-aware u-net for breast lesion segmentation in ultrasound image. IEEE Transact Med Imaging 41(2):476\u2013490","journal-title":"IEEE Transact Med Imaging"},{"issue":"8","key":"1950_CR5","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H, Meng T, Li K, Huang N, Zhang S (2020) A noise-robust framework for automatic segmentation of covid-19 pneumonia lesions from ct images. IEEE Transact Med Imag 39(8):2653\u20132663","journal-title":"IEEE Transact Med Imag"},{"key":"1950_CR6","doi-asserted-by":"crossref","unstructured":"Chu X, Yang W, Ouyang W, Ma C, Yuille AL, Wang X (2017) Multi-context attention for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1831\u20131840","DOI":"10.1109\/CVPR.2017.601"},{"key":"1950_CR7","doi-asserted-by":"crossref","unstructured":"Huang Z, Zhong Z, Sun L, Huo Q (2019) Mask r-cnn with pyramid attention network for scene text detection. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 764\u2013772. IEEE","DOI":"10.1109\/WACV.2019.00086"},{"key":"1950_CR8","doi-asserted-by":"crossref","unstructured":"Gupta A, Agrawal D, Chauhan H, Dolz J, Pedersoli M (2018) An attention model for group-level emotion recognition. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 611\u2013615","DOI":"10.1145\/3242969.3264985"},{"key":"1950_CR9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neucom.2022.07.041","volume":"506","author":"J Liu","year":"2022","unstructured":"Liu J, Zhou W, Cui Y, Yu L, Luo T (2022) Gcnet: Grid-like context-aware network for rgb-thermal semantic segmentation. Neurocomputing 506:60\u201367","journal-title":"Neurocomputing"},{"key":"1950_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1950_CR11","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 424\u2013432 . Springer","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"1950_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, pp 3\u201311"},{"issue":"2","key":"1950_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u2013211","journal-title":"Nat Methods"},{"key":"1950_CR14","doi-asserted-by":"crossref","unstructured":"Cheng Z, Li Y, Chen H, Zhang Z, Pan P, Cheng L (2022) Dsgmffn: Deepest semantically guided multi-scale feature fusion network for automated lesion segmentation in abus images. Computer Methods and Programs in Biomedicine, 106891","DOI":"10.1016\/j.cmpb.2022.106891"},{"issue":"5","key":"1950_CR15","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s13042-021-01459-6","volume":"13","author":"F Cao","year":"2022","unstructured":"Cao F, Gao C, Ye H (2022) A novel method for image segmentation: two-stage decoding network with boundary attention. Int J Mach Learn Cybernet 13(5):1461\u20131473","journal-title":"Int J Mach Learn Cybernet"},{"issue":"5","key":"1950_CR16","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s13042-021-01447-w","volume":"13","author":"K Song","year":"2022","unstructured":"Song K, Zhao Z, Wang J, Qiang Y, Zhao J, Zia MB (2022) Segmentation-based multi-scale attention model for kras mutation prediction in rectal cancer. Int J Mach Learn Cybernet 13(5):1283\u20131299","journal-title":"Int J Mach Learn Cybernet"},{"key":"1950_CR17","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J (2020) Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059. IEEE","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1950_CR18","doi-asserted-by":"crossref","unstructured":"Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331. IEEE","DOI":"10.1109\/ITME.2018.00080"},{"issue":"12","key":"1950_CR19","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Transact Med Imag 37(12):2663\u20132674","journal-title":"IEEE Transact Med Imag"},{"key":"1950_CR20","doi-asserted-by":"crossref","unstructured":"Li S, Liu J, Song Z (2022) Brain tumor segmentation based on region of interest-aided localization and segmentation u-net. International Journal of Machine Learning and Cybernetics, 1\u201311","DOI":"10.21203\/rs.3.rs-627205\/v1"},{"key":"1950_CR21","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1950_CR22","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"1950_CR23","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"1","key":"1950_CR24","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","volume":"25","author":"A Sinha","year":"2020","unstructured":"Sinha A, Dolz J (2020) Multi-scale self-guided attention for medical image segmentation. IEEE J Biomed Health Inform 25(1):121\u2013130","journal-title":"IEEE J Biomed Health Inform"},{"key":"1950_CR25","first-page":"263","volume-title":"International Conference on Medical Image Computing and Computer-assisted Intervention","author":"D-P Fan","year":"2020","unstructured":"Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: Parallel reverse attention network for polyp segmentation. International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, pp 263\u2013273"},{"key":"1950_CR26","doi-asserted-by":"crossref","unstructured":"Yao C, Tang J, Hu M, Wu Y, Guo W, Li Q, Zhang X-P (2021) Claw u-net: a unet variant network with deep feature concatenation for scleral blood vessel segmentation. In: Artificial Intelligence: First CAAI International Conference, CICAI 2021, Hangzhou, China, June 5\u20136, 2021, Proceedings, Part II 1, pp. 67\u201378. Springer","DOI":"10.1007\/978-3-030-93049-3_6"},{"issue":"10","key":"1950_CR27","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Transact Med Imag 38(10):2281\u20132292","journal-title":"IEEE Transact Med Imag"},{"issue":"10","key":"1950_CR28","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","volume":"39","author":"S Feng","year":"2020","unstructured":"Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) Cpfnet: Context pyramid fusion network for medical image segmentation. IEEE Transact Med Imag 39(10):3008\u20133018","journal-title":"IEEE Transact Med Imag"},{"key":"1950_CR29","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"1950_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105121","volume":"190","author":"J Ni","year":"2020","unstructured":"Ni J, Wu J, Tong J, Chen Z, Zhao J (2020) Gc-net: Global context network for medical image segmentation. Comput Methods Programs Biomed 190:105121","journal-title":"Comput Methods Programs Biomed"},{"key":"1950_CR31","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"key":"1950_CR32","doi-asserted-by":"crossref","unstructured":"Wang W, Chen C, Ding M, Yu H, Zha S, Li J (2021) Transbts: Multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 109\u2013119. Springer","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"1950_CR33","doi-asserted-by":"crossref","unstructured":"Gao Y, Zhou M, Metaxas DN (2021) Utnet: a hybrid transformer architecture 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 III 24, pp. 61\u201371. Springer","DOI":"10.1007\/978-3-030-87199-4_6"},{"key":"1950_CR34","first-page":"2441","volume":"36","author":"H Wang","year":"2022","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. Proc. AAAI Conf Artif Intell 36:2441\u20132449","journal-title":"Proc. AAAI Conf Artif Intell"},{"key":"1950_CR35","doi-asserted-by":"crossref","unstructured":"Wang J, Wei L, Wang L, Zhou Q, Zhu L, Qin J (2021) Boundary-aware transformers for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 206\u2013216. Springer","DOI":"10.1007\/978-3-030-87193-2_20"},{"key":"1950_CR36","doi-asserted-by":"crossref","unstructured":"Ji Y, Zhang R, Wang H, Li Z, Wu L, Zhang S, Luo P (2021) Multi-compound transformer for accurate biomedical 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. 326\u2013336. Springer","DOI":"10.1007\/978-3-030-87193-2_31"},{"key":"1950_CR37","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537"},{"key":"1950_CR38","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (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":"1950_CR39","doi-asserted-by":"crossref","unstructured":"Lin A, Chen B, Xu J, Zhang Z, Lu G (2021) Ds-transunet: Dual swin transformer u-net for medical image segmentation. arXiv preprint arXiv:2106.06716","DOI":"10.1109\/TIM.2022.3178991"},{"key":"1950_CR40","unstructured":"Huang X, Deng Z, Li D, Yuan X (2021) Missformer: An effective medical image segmentation transformer. arXiv preprint arXiv:2109.07162"},{"key":"1950_CR41","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"4","key":"1950_CR42","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transact Patte Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Transact Patte Anal Mach Intell"},{"key":"1950_CR43","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 . IEEE","DOI":"10.1109\/3DV.2016.79"},{"key":"1950_CR44","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Sindagi VA, Hacihaliloglu I, Patel VM (2020) Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 363\u2013373 . Springer","DOI":"10.1007\/978-3-030-59719-1_36"},{"key":"1950_CR45","doi-asserted-by":"crossref","unstructured":"Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) Doubleu-net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-based Medical Systems (CBMS), pp. 558\u2013564. IEEE","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"1950_CR46","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"1950_CR47","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"1950_CR48","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH, et al. (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","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"1950_CR49","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229. Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1950_CR50","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347\u201310357. PMLR"},{"key":"1950_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102327","volume":"76","author":"H Wu","year":"2022","unstructured":"Wu H, Chen S, Chen G, Wang W, Lei B, Wen Z (2022) Fat-net: feature adaptive transformers for automated skin lesion segmentation. Med Image Anal 76:102327","journal-title":"Med Image Anal"},{"issue":"3","key":"1950_CR52","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) Segan: adversarial network with multi-scale l1 loss for medical image segmentation. Neuroinformatics 16(3):383\u2013392","journal-title":"Neuroinformatics"},{"key":"1950_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102395","volume":"78","author":"R Wang","year":"2022","unstructured":"Wang R, Chen S, Ji C, Fan J, Li Y (2022) Boundary-aware context neural network for medical image segmentation. Med Image Anal 78:102395","journal-title":"Med Image Anal"},{"key":"1950_CR54","unstructured":"Huang C-H, Wu H-Y, Lin Y-L (2021) Hardnet-mseg: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps. arXiv preprint arXiv:2101.07172"},{"key":"1950_CR55","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"},{"key":"1950_CR56","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1950_CR57","doi-asserted-by":"crossref","unstructured":"Chen C-FR, 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, pp. 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"1950_CR58","doi-asserted-by":"publisher","unstructured":"V\u00e1zquez D, Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, L\u00f3pez AM, Romero A, Drozdzal M, Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of healthcare engineering 2017:1\u20139. https:\/\/doi.org\/10.1155\/2017\/4037190. https:\/\/www.hindawi.com\/journals\/jhe\/2017\/4037190\/","DOI":"10.1155\/2017\/4037190"},{"key":"1950_CR59","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Halvorsen P, Lange Td, Johansen D, Johansen HD (2020) Kvasir-seg: A segmented polyp dataset. In: International Conference on Multimedia Modeling, pp. 451\u2013462. Springer","DOI":"10.1007\/978-3-030-37734-2_37"},{"issue":"2","key":"1950_CR60","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 Transact Med Imag 35(2):630\u2013644","journal-title":"IEEE Transact Med Imag"},{"key":"1950_CR61","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 Imag Graph 43:99\u2013111","journal-title":"Comput Med Imag Graph"},{"issue":"2","key":"1950_CR62","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(2):283\u2013293","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1950_CR63","doi-asserted-by":"crossref","unstructured":"Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255","DOI":"10.1109\/CVPR.2014.39"},{"key":"1950_CR64","doi-asserted-by":"crossref","unstructured":"Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548\u20134557","DOI":"10.1109\/ICCV.2017.487"},{"key":"1950_CR65","doi-asserted-by":"crossref","unstructured":"Fan D-P, Gong C, Cao Y, Ren B, Cheng M-M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421","DOI":"10.24963\/ijcai.2018\/97"},{"key":"1950_CR66","doi-asserted-by":"crossref","unstructured":"Fu S, Lu Y, Wang Y, Zhou Y, Shen W, Fishman E, Yuille A (2020) Domain adaptive relational reasoning for 3d multi-organ segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 656\u2013666. Springer","DOI":"10.1007\/978-3-030-59710-8_64"},{"key":"1950_CR67","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et al. (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"1950_CR68","doi-asserted-by":"crossref","unstructured":"Wang H, Xie S, Lin L, Iwamoto Y, Han X-H, Chen Y-W, Tong R (2022) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2390\u20132394. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"key":"1950_CR69","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, De\u00a0Lange T, Halvorsen P, Johansen HD (2019) Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225\u20132255. IEEE","DOI":"10.1109\/ISM46123.2019.00049"},{"issue":"12","key":"1950_CR70","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transact Patt Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Transact Patt Anal Mach Intell"},{"key":"1950_CR71","doi-asserted-by":"publisher","first-page":"40496","DOI":"10.1109\/ACCESS.2021.3063716","volume":"9","author":"D Jha","year":"2021","unstructured":"Jha D, Ali S, Tomar NK, Johansen HD, Johansen D, Rittscher J, Riegler MA, Halvorsen P (2021) Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access 9:40496\u201340510","journal-title":"IEEE Access"},{"issue":"5","key":"1950_CR72","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1950_CR73","doi-asserted-by":"crossref","unstructured":"Fang Y, Chen C, Yuan Y, Tong K-y (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 302\u2013310. Springer","DOI":"10.1007\/978-3-030-32239-7_34"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01950-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01950-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01950-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T10:28:28Z","timestamp":1707560908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01950-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":73,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1950"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01950-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,20]]},"assertion":[{"value":"9 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}