{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:50:17Z","timestamp":1784631017215,"version":"3.55.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172403"],"award-info":[{"award-number":["62172403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012326","name":"International Science and Technology Cooperation Programme","doi-asserted-by":"publisher","award":["2019A050510030"],"award-info":[{"award-number":["2019A050510030"]}],"id":[{"id":"10.13039\/501100012326","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Distinguished Young Scholars Fund of Guangdong","award":["2021B1515020019"],"award-info":[{"award-number":["2021B1515020019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["61872351"],"award-info":[{"award-number":["61872351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-021-06816-8","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T13:02:37Z","timestamp":1646571757000},"page":"8657-8669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Brain stroke lesion segmentation using consistent perception generative adversarial network"],"prefix":"10.1007","volume":"34","author":[{"given":"Shuqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senrong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingchuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiying","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,6]]},"reference":[{"key":"6816_CR1","doi-asserted-by":"crossref","unstructured":"Abraham N, Khan NM (2019) A novel focal tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683\u2013687. IEEE","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"6816_CR2","unstructured":"Bang D, Shim H (2018) Improved training of generative adversarial networks using representative features. arXiv preprint arXiv:1801.09195"},{"key":"6816_CR3","doi-asserted-by":"crossref","unstructured":"Baur C, Albarqouni S, Navab N (2017) Semi-supervised deep learning for fully convolutional networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 311\u2013319. Springer","DOI":"10.1007\/978-3-319-66179-7_36"},{"key":"6816_CR4","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp. 801\u2013818. Cham","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"6816_CR5","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/TMI.2019.2962786","volume":"39","author":"MT Chen","year":"2019","unstructured":"Chen MT, Mahmood F, Sweer JA, Durr NJ (2019) Ganpop: generative adversarial network prediction of optical properties from single snapshot wide-field images. IEEE Trans Med Imag 39:1988","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR6","doi-asserted-by":"crossref","unstructured":"Chen S, Bortsova G, Ju\u00e1rez AGU, van Tulder G, de\u00a0Bruijne M (2019) Multi-task attention-based semi-supervised learning for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 457\u2013465. Springer","DOI":"10.1007\/978-3-030-32248-9_51"},{"key":"6816_CR7","doi-asserted-by":"crossref","unstructured":"Chen T, Zhai X, Ritter M, Lucic M, Houlsby N (2019) Self-supervised gans via auxiliary rotation loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12154\u201312163","DOI":"10.1109\/CVPR.2019.01243"},{"key":"6816_CR8","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TMI.2019.2935409","volume":"39","author":"X Chen","year":"2019","unstructured":"Chen X, Lian C, Wang L, Deng H, Fung SH, Nie D, Thung KH, Yap PT, Gateno J, Xia JJ et al (2019) One-shot generative adversarial learning for mri segmentation of craniomaxillofacial bony structures. IEEE Trans Med Imag 39:787","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR9","unstructured":"Cohen TS, Welling M (2016) Group equivariant convolutional networks. arXiv: Learning"},{"issue":"10","key":"6816_CR10","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","volume":"38","author":"SU Dar","year":"2019","unstructured":"Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, \u00c7ukur T (2019) Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Trans Med Imag 38(10):2375\u20132388","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR11","unstructured":"Dieleman S, De\u00a0Fauw J, Kavukcuoglu K (2016) Exploiting cyclic symmetry in convolutional neural networks. arXiv: Learning"},{"key":"6816_CR12","doi-asserted-by":"publisher","first-page":"101631","DOI":"10.1016\/j.media.2019.101631","volume":"60","author":"M Dunnhofer","year":"2020","unstructured":"Dunnhofer M, Antico M, Sasazawa F, Takeda Y, Camps S, Martinel N, Micheloni C, Carneiro G, Fontanarosa D (2020) Siam-u-net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Med Image Anal 60:101631","journal-title":"Med Image Anal"},{"issue":"9913","key":"6816_CR13","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0140-6736(13)61953-4","volume":"383","author":"VL Feigin","year":"2014","unstructured":"Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, Moran AE, Sacco RL, Anderson L, Truelsen T et al (2014) Global and regional burden of stroke during 1990\u20132010: findings from the global burden of disease study 2010. The Lancet 383(9913):245\u2013255","journal-title":"The Lancet"},{"key":"6816_CR14","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"10","key":"6816_CR15","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/TMI.2019.2899364","volume":"38","author":"M Gadermayr","year":"2019","unstructured":"Gadermayr M, Gupta L, Appel V, Boor P, Klinkhammer BM, Merhof D (2019) Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology. IEEE Trans Med Imag 38(10):2293\u20132302","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR16","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672\u20132680","journal-title":"Adv Neural Inf Process Syst"},{"issue":"10","key":"6816_CR17","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 Trans Med Imag 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR18","unstructured":"Hasan S, Linte CA (2019) U-netplus: a modified encoder-decoder u-net architecture for semantic and instance segmentation of surgical instrument. arXiv preprint arXiv:1902.08994"},{"key":"6816_CR19","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1109\/TMI.2019.2940555","volume":"39","author":"Y Hiasa","year":"2019","unstructured":"Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical ct using bayesian u-net for personalized musculoskeletal modeling. IEEE Trans Med Imag 39:1030","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR20","doi-asserted-by":"crossref","unstructured":"Hu S, Shen Y, Wang S, Lei B (2020) Brain mr to pet synthesis via bidirectional generative adversarial network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 698\u2013707. Springer, Cham","DOI":"10.1007\/978-3-030-59713-9_67"},{"key":"6816_CR21","doi-asserted-by":"crossref","unstructured":"Hu S, Yu W, Chen Z, Wang S (2020) Medical image reconstruction using generative adversarial network for alzheimer disease assessment with class-imbalance problem. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1323\u20131327. IEEE","DOI":"10.1109\/ICCC51575.2020.9344912"},{"key":"6816_CR22","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"6816_CR23","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen YW, 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"},{"issue":"9","key":"6816_CR24","doi-asserted-by":"publisher","first-page":"634","DOI":"10.2471\/BLT.16.181636","volume":"94","author":"W Johnson","year":"2016","unstructured":"Johnson W, Onuma O, Owolabi M, Sachdev S (2016) Stroke: a global response is needed. Bull World Health Organ 94(9):634","journal-title":"Bull World Health Organ"},{"issue":"9","key":"6816_CR25","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1161\/01.STR.0000087172.16305.CD","volume":"34","author":"G Kwakkel","year":"2003","unstructured":"Kwakkel G, Kollen BJ, van der Grond J, Prevo AJ (2003) Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke 34(9):2181\u20132186","journal-title":"Stroke"},{"key":"6816_CR26","doi-asserted-by":"publisher","first-page":"101716","DOI":"10.1016\/j.media.2020.101716","volume":"64","author":"B Lei","year":"2020","unstructured":"Lei B, Xia Z, Jiang F, Jiang X, Ge Z, Xu Y, Qin J, Chen S, Wang T, Wang S (2020) Skin lesion segmentation via generative adversarial networks with dual discriminators. Med Image Anal 64:101716","journal-title":"Med Image Anal"},{"issue":"12","key":"6816_CR27","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 CW, Heng PA (2018) H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans Med Imag 37(12):2663\u20132674","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR28","doi-asserted-by":"crossref","unstructured":"Li Z, Wang Y, Yu J (2017) Brain tumor segmentation using an adversarial network. In: International MICCAI brainlesion workshop, pp. 123\u2013132. Springer, Cham","DOI":"10.1007\/978-3-319-75238-9_11"},{"key":"6816_CR29","doi-asserted-by":"publisher","first-page":"180011","DOI":"10.1038\/sdata.2018.11","volume":"5","author":"SL Liew","year":"2018","unstructured":"Liew SL, Anglin JM, Banks NW, Sondag M, Ito KL, Kim H, Chan J, Ito J, Jung C, Khoshab N et al (2018) A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientif Data 5:180011","journal-title":"Scientif Data"},{"key":"6816_CR30","doi-asserted-by":"crossref","unstructured":"Madani A, Moradi M, Karargyris A, Syeda-Mahmood T (2018) Semi-supervised learning with generative adversarial networks for chest x-ray classification with ability of data domain adaptation. In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), pp. 1038\u20131042. IEEE","DOI":"10.1109\/ISBI.2018.8363749"},{"issue":"8","key":"6816_CR31","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMI.2019.2911588","volume":"38","author":"Y Man","year":"2019","unstructured":"Man Y, Huang Y, Feng J, Li X, Wu F (2019) Deep q learning driven ct pancreas segmentation with geometry-aware u-net. IEEE Trans Med Imag 38(8):1971\u20131980","journal-title":"IEEE Trans Med Imag"},{"issue":"4","key":"6816_CR32","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TMI.2015.2502596","volume":"35","author":"BH Menze","year":"2015","unstructured":"Menze BH, Van Leemput K, Lashkari D, Riklin-Raviv T, Geremia E, Alberts E, Gruber P, Wegener S, Weber MA, Sz\u00e9kely G et al (2015) A generative probabilistic model and discriminative extensions for brain lesion segmentation-with application to tumor and stroke. IEEE Trans Med Imag 35(4):933\u2013946","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR33","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/TMI.2019.2899364","volume":"38","author":"G Michael","year":"2019","unstructured":"Michael G, Laxmi G, Vitus A, Peter B, Barbara M (2019) Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology. IEEE Trans Med Imag 38:2293","journal-title":"IEEE Trans Med Imag"},{"issue":"12","key":"6816_CR34","doi-asserted-by":"publisher","first-page":"e834","DOI":"10.1016\/j.na.2008.12.006","volume":"71","author":"LF Mo","year":"2009","unstructured":"Mo LF, Wang SQ (2009) A variational approach to nonlinear two-point boundary value problems. Nonlinear Anal: Theory, Methods Appl 71(12):e834\u2013e838","journal-title":"Nonlinear Anal: Theory, Methods Appl"},{"issue":"5","key":"6816_CR35","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1109\/TNNLS.2018.2870182","volume":"30","author":"D Nie","year":"2018","unstructured":"Nie D, Wang L, Gao Y, Lian J, Shen D (2018) Strainet: Spatially varying stochastic residual adversarial networks for mri pelvic organ segmentation. IEEE Trans Neural Netw Learn Syst 30(5):1552\u20131564","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6816_CR36","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et\u00a0al. (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"6816_CR37","doi-asserted-by":"crossref","unstructured":"Qi K, Yang H, Li C, Liu Z, Wang M, Liu Q, Wang S (2019) X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 247\u2013255. Springer","DOI":"10.1007\/978-3-030-32248-9_28"},{"key":"6816_CR38","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":"6816_CR39","doi-asserted-by":"crossref","unstructured":"Sedai S, Mahapatra D, Hewavitharanage S, Maetschke S, Garnavi R (2017) Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 75\u201382. Springer","DOI":"10.1007\/978-3-319-66185-8_9"},{"issue":"4","key":"6816_CR40","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TMI.2019.2945521","volume":"39","author":"A Sharma","year":"2019","unstructured":"Sharma A, Hamarneh G (2019) Missing mri pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans Med Imag 39(4):1170\u20131183","journal-title":"IEEE Trans Med Imag"},{"issue":"02","key":"6816_CR41","doi-asserted-by":"publisher","first-page":"1750036","DOI":"10.1142\/S0129065717500368","volume":"28","author":"S Wang","year":"2018","unstructured":"Wang S, Hu Y, Shen Y, Li H (2018) Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning. Int J Neural Syst 28(02):1750036","journal-title":"Int J Neural Syst"},{"key":"6816_CR42","doi-asserted-by":"publisher","first-page":"29979","DOI":"10.1109\/ACCESS.2018.2843392","volume":"6","author":"S Wang","year":"2018","unstructured":"Wang S, Shen Y, Shi C, Yin P, Wang Z, Cheung PWH, Cheung JPY, Luk KDK, Hu Y (2018) Skeletal maturity recognition using a fully automated system with convolutional neural networks. IEEE Access 6:29979\u201329993","journal-title":"IEEE Access"},{"key":"6816_CR43","doi-asserted-by":"crossref","unstructured":"Wang S, Wang H, Shen Y, Wang X (2018) Automatic recognition of mild cognitive impairment and alzheimers disease using ensemble based 3d densely connected convolutional networks. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 517\u2013523. IEEE","DOI":"10.1109\/ICMLA.2018.00083"},{"key":"6816_CR44","doi-asserted-by":"crossref","unstructured":"Wang S, Wang X, Hu Y, Shen Y, Yang Z, Gan M, Lei B (2020) Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Transactions on Automation Science and Engineering","DOI":"10.1109\/TASE.2020.2981637"},{"issue":"11\u201312","key":"6816_CR45","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.1016\/j.camwa.2009.03.050","volume":"58","author":"SQ Wang","year":"2009","unstructured":"Wang SQ (2009) A variational approach to nonlinear two-point boundary value problems. Computers Math Appl 58(11\u201312):2452\u20132455","journal-title":"Computers Math Appl"},{"issue":"3","key":"6816_CR46","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.physleta.2007.02.049","volume":"367","author":"SQ Wang","year":"2007","unstructured":"Wang SQ, He JH (2007) Variational iteration method for solving integro-differential equations. Phys Lett A 367(3):188\u2013191","journal-title":"Phys Lett A"},{"issue":"1","key":"6816_CR47","first-page":"741","volume":"6","author":"SQ Wang","year":"2008","unstructured":"Wang SQ, He JH (2008) Variational iteration method for a nonlinear reaction-diffusion process. Int J Chem Reactor Eng 6(1):741","journal-title":"Int J Chem Reactor Eng"},{"key":"6816_CR48","doi-asserted-by":"crossref","unstructured":"Wang Z, Zou N, Shen D, Ji S (2020) Non-local u-nets for biomedical image segmentation. In: Thirty-Fourth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v34i04.6100"},{"issue":"12","key":"6816_CR49","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1109\/TMI.2017.2708987","volume":"36","author":"JM Wolterink","year":"2017","unstructured":"Wolterink JM, Leiner T, Viergever MA, I\u0161gum I (2017) Generative adversarial networks for noise reduction in low-dose ct. IEEE Trans Med Imag 36(12):2536\u20132545","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR50","doi-asserted-by":"crossref","unstructured":"Worrall DE, Garbin SJ, Turmukhambetov D, Brostow GJ (2017) Harmonic networks: Deep translation and rotation equivariance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5028\u20135037","DOI":"10.1109\/CVPR.2017.758"},{"issue":"3\u20134","key":"6816_CR51","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 l 1 loss for medical image segmentation. Neuroinformatics 16(3\u20134):383\u2013392","journal-title":"Neuroinformatics"},{"key":"6816_CR52","doi-asserted-by":"crossref","unstructured":"Yang H, Huang W, Qi K, Li C, Liu X, Wang M, Zheng H, Wang S (2019) Clci-net: Cross-level fusion and context inference networks for lesion segmentation of chronic stroke. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 266\u2013274. Springer","DOI":"10.1007\/978-3-030-32248-9_30"},{"issue":"6","key":"6816_CR53","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2018","unstructured":"Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, Zhang Y, Sun L, Wang G (2018) Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans Med Imag 37(6):1348\u20131357","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR54","unstructured":"You S, Liu Y, Lei B, Wang S (2020) Fine perceptive gans for brain mr image super-resolution in wavelet domain. arXiv preprint arXiv:2011.04145"},{"issue":"7","key":"6816_CR55","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TMI.2019.2895894","volume":"38","author":"B Yu","year":"2019","unstructured":"Yu B, Zhou L, Wang L, Shi Y, Fripp J, Bourgeat P (2019) Ea-gans: edge-aware generative adversarial networks for cross-modality mr image synthesis. IEEE Trans Med Imag 38(7):1750\u20131762","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR56","first-page":"777","volume":"47","author":"W Yu","year":"2021","unstructured":"Yu W, Lei B, Ng MK, Cheung AC, Shen Y, Wang S (2021) Tensorizing gan with high-order pooling for alzheimer\u2019s disease assessment. IEEE Trans Neural Netw Learn Syst 47:777","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6816_CR57","doi-asserted-by":"crossref","unstructured":"Zhang L, Gooya A, Frangi AF (2017) Semi-supervised assessment of incomplete lv coverage in cardiac mri using generative adversarial nets. In: International Workshop on Simulation and Synthesis in Medical Imaging, pp. 61\u201368. Springer","DOI":"10.1007\/978-3-319-68127-6_7"},{"issue":"9","key":"6816_CR58","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1109\/TMI.2018.2821244","volume":"37","author":"R Zhang","year":"2018","unstructured":"Zhang R, Zhao L, Lou W, Abrigo JM, Mok VC, Chu WC, Wang D, Shi L (2018) Automatic segmentation of acute ischemic stroke from dwi using 3-d fully convolutional densenets. IEEE Trans Med Imag 37(9):2149\u20132160","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR59","doi-asserted-by":"crossref","unstructured":"Zhao M, Wang L, Chen J, Nie D, Cong Y, Ahmad S, Ho A, Yuan P, Fung SH, Deng HH, et\u00a0al. (2018) Craniomaxillofacial bony structures segmentation from mri with deep-supervision adversarial learning. In: International conference on medical image computing and computer-assisted intervention, pp. 720\u2013727. Springer","DOI":"10.1007\/978-3-030-00937-3_82"},{"key":"6816_CR60","doi-asserted-by":"crossref","unstructured":"Zheng H, Lin L, Hu H, Zhang Q, Chen Q, Iwamoto Y, Han X, Chen YW, Tong R, Wu J (2019) Semi-supervised segmentation of liver using adversarial learning with deep atlas prior. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 148\u2013156. Springer","DOI":"10.1007\/978-3-030-32226-7_17"},{"key":"6816_CR61","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, 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, pp. 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"6816_CR62","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1109\/TMI.2019.2935018","volume":"39","author":"Q Zhu","year":"2019","unstructured":"Zhu Q, Du B, Yan P (2019) Boundary-weighted domain adaptive neural network for prostate mr image segmentation. IEEE Trans Med Imag 39:753","journal-title":"IEEE Trans Med Imag"},{"key":"6816_CR63","doi-asserted-by":"crossref","unstructured":"Zhu W, Xiang X, Tran TD, Hager GD, Xie X (2018) Adversarial deep structured nets for mass segmentation from mammograms. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp. 847\u2013850. IEEE","DOI":"10.1109\/ISBI.2018.8363704"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06816-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06816-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06816-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T05:49:42Z","timestamp":1652507382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06816-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,6]]},"references-count":63,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6816"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06816-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,6]]},"assertion":[{"value":"6 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}