{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:04:05Z","timestamp":1776179045167,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972274"],"award-info":[{"award-number":["61972274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M631774"],"award-info":[{"award-number":["2018M631774"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taiyuan 2019-nCoV prophylaxis and treatment research project","award":["XG2020-5-04"],"award-info":[{"award-number":["XG2020-5-04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s10489-021-03025-7","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:03:47Z","timestamp":1642032227000},"page":"10369-10383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification"],"prefix":"10.1007","volume":"52","author":[{"given":"Jun","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xiaosong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Song","sequence":"additional","affiliation":[]},{"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"3025_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.knosys.2016.06.010","volume":"107","author":"UR Acharya","year":"2016","unstructured":"Acharya UR, Chowriappa P, Fujita H, Bhat S, Dua S, Koh JE, Eugene L, Kongmebhol P, Ng KH (2016) Thyroid lesion classification in 242 patient population using gabor transform features from high resolution ultrasound images. Knowl-Based Syst 107:235\u2013245","journal-title":"Knowl-Based Syst"},{"issue":"99","key":"3025_CR2","first-page":"1","volume":"PP","author":"D Avola","year":"2021","unstructured":"Avola D, Cinque L, Fagioli A, Filetti S, Rodola E (2021) Multimodal feature fusion and knowledge-driven learning via experts consult for thyroid nodule classification. IEEE Trans Circ Syst Video Technol PP (99):1\u20131","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"3025_CR3","unstructured":"Brock A, Donahue J, Simonyan K (2018) Large scale gan training for high fidelity natural image synthesis. In: International conference on learning representations"},{"issue":"3","key":"3025_CR4","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3758\/BF03193020","volume":"39","author":"JA Bullinaria","year":"2007","unstructured":"Bullinaria JA, Levy JP (2007) Extracting semantic representations from word co-occurrence statistics: a computational study. Behav Res Methods 39(3):510\u2013526","journal-title":"Behav Res Methods"},{"issue":"7","key":"3025_CR5","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A (2017) Deep learning: a primer for radiologists. Radiographics 37(7):2113\u20132131","journal-title":"Radiographics"},{"key":"3025_CR6","doi-asserted-by":"crossref","unstructured":"Chen C, Dou Q, Chen H, Heng PA (2018) Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest x-ray segmentation. In: International workshop on machine learning in medical imaging. Springer, pp 143\u2013151","DOI":"10.1007\/978-3-030-00919-9_17"},{"issue":"7","key":"3025_CR7","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","volume":"39","author":"C Chen","year":"2020","unstructured":"Chen C, Dou Q, Chen H, Qin J, Heng PA (2020) Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans Med Imaging 39(7):2494\u20132505","journal-title":"IEEE Trans Med Imaging"},{"key":"3025_CR8","doi-asserted-by":"publisher","first-page":"105329","DOI":"10.1016\/j.cmpb.2020.105329","volume":"185","author":"J Chen","year":"2020","unstructured":"Chen J, You H, Li K (2020) A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Comput Methods Programs Biomed 185:105329","journal-title":"Comput Methods Programs Biomed"},{"key":"3025_CR9","doi-asserted-by":"crossref","unstructured":"Dong H, Yu S, Wu C, Guo Y (2017) Semantic image synthesis via adversarial learning. In: Proceedings of the IEEE international conference on computer vision, pp 5706\u20135714","DOI":"10.1109\/ICCV.2017.608"},{"issue":"10","key":"3025_CR10","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2016) Lstm: A search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222\u20132232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"3025_CR11","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1109\/JBHI.2019.2942429","volume":"24","author":"Y Gu","year":"2019","unstructured":"Gu Y, Ge Z, Bonnington CP, Zhou J (2019) Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J Biomed Health Inform 24(5):1379\u20131393","journal-title":"IEEE J Biomed Health Inform"},{"key":"3025_CR12","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"},{"issue":"5","key":"3025_CR13","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1007\/s10278-020-00362-w","volume":"33","author":"SW Kwon","year":"2020","unstructured":"Kwon SW, Choi IJ, Kang JY, Jang WI, Lee GH, Lee MC (2020) Ultrasonographic thyroid nodule classification using a deep convolutional neural network with surgical pathology. J Digit Imaging 33 (5):1202\u20131208","journal-title":"J Digit Imaging"},{"issue":"1","key":"3025_CR14","first-page":"1","volume":"8","author":"H Li","year":"2018","unstructured":"Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, Liu W, Zhang J (2018) An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Scientif Rep 8(1):1\u201312","journal-title":"Scientif Rep"},{"key":"3025_CR15","doi-asserted-by":"crossref","unstructured":"Li Z, Yang K, Zhang L, Wei C, Yang P, Xu W (2020) Classification of thyroid nodules with stacked denoising sparse autoencoder. Int J Endocrinol 2020","DOI":"10.1155\/2020\/9015713"},{"key":"3025_CR16","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.ultras.2016.09.011","volume":"73","author":"J Ma","year":"2017","unstructured":"Ma J, Wu F, Zhu J, Xu D, Kong D (2017) A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 73:221\u2013230","journal-title":"Ultrasonics"},{"key":"3025_CR17","unstructured":"Mescheder L, Geiger A, Nowozin S (2018) Which training methods for gans do actually converge?. In: International conference on machine learning. PMLR, pp 3481\u20133490"},{"key":"3025_CR18","doi-asserted-by":"crossref","unstructured":"Messina N, Falchi F, Esuli A, Amato G (2021) Transformer reasoning network for image-text matching and retrieval. In: 2020 25Th international conference on pattern recognition (ICPR). IEEE, pp 5222\u20135229","DOI":"10.1109\/ICPR48806.2021.9413172"},{"key":"3025_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.patrec.2018.03.026","volume":"110","author":"D Moujahid","year":"2018","unstructured":"Moujahid D, Elharrouss O, Tairi H (2018) Visual object tracking via the local soft cosine similarity. Pattern Recogn Lett 110:79\u2013 85","journal-title":"Pattern Recogn Lett"},{"key":"3025_CR20","unstructured":"Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: ICML\u201917 Proceedings of the 34th International Conference on Machine Learning - vol 70, pp 2642\u20132651"},{"key":"3025_CR21","unstructured":"Oord AVD, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. arXiv:1807.03748"},{"key":"3025_CR22","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neucom.2019.11.011","volume":"381","author":"C Pan","year":"2020","unstructured":"Pan C, Huang J, Hao J, Gong J (2020) Towards zero-shot learning generalization via a cosine distance loss. Neurocomputing 381:167\u2013176","journal-title":"Neurocomputing"},{"key":"3025_CR23","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.compmedimag.2018.10.001","volume":"71","author":"A Prochazka","year":"2019","unstructured":"Prochazka A, Gulati S, Holinka S, Smutek D (2019) Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition. Comput Med Imaging Graph 71:9\u2013 18","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"3025_CR24","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/JBHI.2019.2950994","volume":"24","author":"P Qin","year":"2020","unstructured":"Qin P, Wu K, Hu Y, Zeng J, Chai X (2020) Diagnosis of benign and malignant thyroid nodules using combined conventional ultrasound and ultrasound elasticity imaging. IEEE J Biomed Health Inform 24 (4):1028\u20131036","journal-title":"IEEE J Biomed Health Inform"},{"key":"3025_CR25","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR 2016 : International Conference on learning representations 2016"},{"key":"3025_CR26","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compbiomed.2018.02.002","volume":"95","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra U, Gudigar A, Maithri M, Gertych A, Meiburger KM, Yeong CH, Madla C, Kongmebhol P, Molinari F, Ng KH et al (2018) Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images. Comput Biol Med 95:55\u201362","journal-title":"Comput Biol Med"},{"key":"3025_CR27","doi-asserted-by":"crossref","unstructured":"Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X (2018) Adversarial domain adaptation for classification of prostate histopathology whole-slide images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 201\u2013209","DOI":"10.1007\/978-3-030-00934-2_23"},{"key":"3025_CR28","doi-asserted-by":"publisher","first-page":"105611","DOI":"10.1016\/j.cmpb.2020.105611","volume":"196","author":"G Shi","year":"2020","unstructured":"Shi G, Wang J, Qiang Y, Yang X, Zhao J, Hao R, Yang W, Du Q, Kazihise NGF (2020) Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Comput Methods Prog Biomed 196 :105611","journal-title":"Comput Methods Prog Biomed"},{"key":"3025_CR29","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR 2015 : International Conference on learning representations 2015"},{"key":"3025_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3025_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"5","key":"3025_CR32","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.jacr.2017.01.046","volume":"14","author":"FN Tessler","year":"2017","unstructured":"Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC et al (2017) Acr thyroid imaging, reporting and data system (ti-rads): white paper of the acr ti-rads committee. J Amer college Radiol 14(5):587\u2013595","journal-title":"J Amer college Radiol"},{"issue":"10","key":"3025_CR33","doi-asserted-by":"publisher","first-page":"2629","DOI":"10.1007\/s11263-020-01338-7","volume":"128","author":"E Ververas","year":"2020","unstructured":"Ververas E, Zafeiriou S (2020) Slidergan: Synthesizing expressive face images by sliding 3d blendshape parameters. Int J Comput Vis 128(10):2629\u20132650","journal-title":"Int J Comput Vis"},{"issue":"2","key":"3025_CR34","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1109\/JBHI.2019.2928369","volume":"24","author":"H Wang","year":"2019","unstructured":"Wang H, Jia H, Lu L, Xia Y (2019) Thorax-net: an attention regularized deep neural network for classification of thoracic diseases on chest radiography. IEEE J Biomed Health Inform 24(2):475\u2013485","journal-title":"IEEE J Biomed Health Inform"},{"key":"3025_CR35","doi-asserted-by":"publisher","first-page":"101846","DOI":"10.1016\/j.media.2020.101846","volume":"67","author":"H Wang","year":"2021","unstructured":"Wang H, Wang S, Qin Z, Zhang Y, Li R, Xia Y (2021) Triple attention learning for classification of 14 thoracic diseases using chest radiography. Med Image Anal 67:101846","journal-title":"Med Image Anal"},{"key":"3025_CR36","doi-asserted-by":"crossref","unstructured":"Wang J, Li S, Song W, Qin H, Zhang B, Hao A (2018) Learning from weakly-labeled clinical data for automatic thyroid nodule classification in ultrasound images. In: 2018 25Th IEEE international conference on image processing (ICIP), pp 3114\u20133118","DOI":"10.1109\/ICIP.2018.8451085"},{"key":"3025_CR37","doi-asserted-by":"publisher","first-page":"101665","DOI":"10.1016\/j.media.2020.101665","volume":"61","author":"L Wang","year":"2020","unstructured":"Wang L, Zhang L, Zhu M, Qi X, Yi Z (2020) Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal 61:101665","journal-title":"Med Image Anal"},{"key":"3025_CR38","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang M, Deng W (2018) Deep visual domain adaptation: a survey. Neurocomputing 312:135\u2013153","journal-title":"Neurocomputing"},{"key":"3025_CR39","doi-asserted-by":"crossref","unstructured":"Wang X, Gupta A (2015) Unsupervised learning of visual representations using videos. In: Proceedings of the IEEE international conference on computer vision, pp 2794\u20132802","DOI":"10.1109\/ICCV.2015.320"},{"key":"3025_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Li L, Ye W, Long M, Wang J (2019) Transferable attention for domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 5345\u20135352","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"3025_CR41","doi-asserted-by":"crossref","unstructured":"Xie X, Niu J, Liu X, Chen Z, Tang S (2020) A survey on domain knowledge powered deep learning for medical image analysis. arXiv:2004.12150","DOI":"10.1016\/j.media.2021.101985"},{"key":"3025_CR42","doi-asserted-by":"crossref","unstructured":"Xu T, Zhang P, Huang Q, Zhang H, Gan Z, Huang X, He X (2018) Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1316\u20131324","DOI":"10.1109\/CVPR.2018.00143"},{"key":"3025_CR43","doi-asserted-by":"crossref","unstructured":"Yang W, Zhao J, Qiang Y, Yang X, Dong Y, Du Q, Shi G, Zia MB (2019) Dscgans: Integrate domain knowledge in training dual-path semi-supervised conditional generative adversarial networks and s3vm for ultrasonography thyroid nodules classification. In: International conference on medical image computing and computer-assisted intervention, pp 558\u2013566","DOI":"10.1007\/978-3-030-32251-9_61"},{"key":"3025_CR44","doi-asserted-by":"crossref","unstructured":"Yao Y, Zhang Y, Li X, Ye Y (2019) Heterogeneous domain adaptation via soft transfer network. In: Proceedings of the 27th ACM international conference on multimedia, pp 1578\u20131586","DOI":"10.1145\/3343031.3350955"},{"key":"3025_CR45","doi-asserted-by":"publisher","first-page":"101552","DOI":"10.1016\/j.media.2019.101552","volume":"58","author":"X Yi","year":"2019","unstructured":"Yi X, Walia E, Babyn P (2019) Generative adversarial network in medical imaging: a review. Medical image analysis 58:101552","journal-title":"Medical image analysis"},{"key":"3025_CR46","doi-asserted-by":"crossref","unstructured":"Yin G, Liu B, Sheng L, Yu N, Wang X, Shao J (2019) Semantics disentangling for text-to-image generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2327\u20132336","DOI":"10.1109\/CVPR.2019.00243"},{"key":"3025_CR47","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR, pp 7354\u20137363"},{"key":"3025_CR48","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu T, Li H (2017) Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: 2017 IEEE International conference on computer vision (ICCV), pp 5908\u20135916","DOI":"10.1109\/ICCV.2017.629"},{"key":"3025_CR49","doi-asserted-by":"publisher","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wei Y, Wu Q, Zhao P, Niu S, Huang J, Tan M (2020) Collaborative unsupervised domain adaptation for medical image diagnosis. IEEE Trans Image Process 29:7834\u20137844","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"3025_CR50","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TBME.2020.2971065","volume":"67","author":"H Zhou","year":"2020","unstructured":"Zhou H, Wang K, Tian J (2020) Online transfer learning for differential diagnosis of benign and malignant thyroid nodules with ultrasound images. IEEE Trans Biomed Eng 67(10):2773\u20132780","journal-title":"IEEE Trans Biomed Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03025-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03025-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03025-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T10:27:59Z","timestamp":1744194479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03025-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,13]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["3025"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03025-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,13]]},"assertion":[{"value":"18 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2022","order":2,"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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}