{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:05:49Z","timestamp":1793484349942,"version":"4.5.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, the incidence of thyroid cancer has increased dramatically, resulting in an increased demand for early thyroid nodule examination. Ultrasound (US) imaging is the modality most frequently used to image thyroid nodules; However, the low image resolution, speckle noise, and high variability make it difficult to utilize traditional image processing techniques. Recent advances in deep learning (DL) have increased research into the automated processing of thyroid US images. We review three main image processing tasks for thyroid nodule analysis: classification, segmentation, and detection. We discuss the advantages and limitations of the recently proposed DL techniques as well as the data availability and algorithmic efficacy. In addition, we investigate the remaining obstacles and future potential for automated analysis of thyroid US images.<\/jats:p>","DOI":"10.1007\/s10462-023-10635-9","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T05:02:36Z","timestamp":1707714156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep learning for thyroid nodule examination: a technical review"],"prefix":"10.1007","volume":"57","author":[{"given":"Debottama","family":"Das","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Sriram","family":"Iyengar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad S.","family":"Majdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey J.","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Alsayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"10635_CR2","doi-asserted-by":"publisher","first-page":"103871","DOI":"10.1016\/j.compbiomed.2020.103871","volume":"122","author":"F Abdolali","year":"2020","unstructured":"Abdolali F, Kapur J, Jaremko JL, Noga M, Hareendranathan AR, Punithakumar K (2020) Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks. Comput Biol Med 122:103871. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103871","journal-title":"Comput Biol Med"},{"key":"10635_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5052435","author":"S Anari","year":"2022","unstructured":"Anari S, Tataei Sarshar N, Mahjoori N, Dorosti S, Rezaie A (2022) Review of deep learning approaches for thyroid cancer diagnosis. Math Probl Eng. https:\/\/doi.org\/10.1155\/2022\/5052435","journal-title":"Math Probl Eng"},{"issue":"6","key":"10635_CR4","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s11548-015-1181-6","volume":"10","author":"EMA Anas","year":"2015","unstructured":"Anas EMA, Seitel A, Rasoulian A, John PS, Pichora D, Darras K et al (2015) Bone enhancement in ultrasound using local spectrum variations for guiding percutaneous scaphoid fracture fixation procedures. Int J Comput Assist Radiol Surg 10(6):959\u2013969. https:\/\/doi.org\/10.1007\/s11548-015-1181-6","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10635_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-6862-0_30","author":"S Baldota","year":"2021","unstructured":"Baldota S, Malathy C (2021) Classification of ultrasound thyroid nodule images by computer-aided diagnosis: a technical review. Comput vis Bio-Inspir Comput. https:\/\/doi.org\/10.1007\/978-981-33-6862-0_30","journal-title":"Comput vis Bio-Inspir Comput"},{"issue":"3","key":"10635_CR6","doi-asserted-by":"publisher","first-page":"153","DOI":"10.3109\/07853890008998821","volume":"32","author":"TH Brix","year":"2000","unstructured":"Brix TH, Heged\u00fcs L (2000) Genetic and environmental factors in the aetiology of simple goiter. Ann Med 32(3):153\u2013156","journal-title":"Ann Med"},{"issue":"3","key":"10635_CR7","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1148\/radiol.2019181343","volume":"292","author":"M Buda","year":"2019","unstructured":"Buda M, Wildman-Tobriner B, Hoang JK, Thayer D, Tessler FN, Middleton WD, Mazurowski MA (2019) Management of thyroid nodules seen on us images: deep learning may match performance of radiologists. Radiology 292(3):695\u2013701. https:\/\/doi.org\/10.1148\/radiol.2019181343","journal-title":"Radiology"},{"issue":"8","key":"10635_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21037\/aot.2020.04.01","volume":"5","author":"YJ Chai","year":"2020","unstructured":"Chai YJ, Song J, Shaear M, Yi KH (2020) Artificial intelligence for thyroid nodule ultrasound image analysis. Ann Thyroid 5(8):1\u20135. https:\/\/doi.org\/10.21037\/aot.2020.04.01","journal-title":"Ann Thyroid"},{"issue":"6","key":"10635_CR9","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TBME.2010.2041003","volume":"57","author":"CY Chang","year":"2010","unstructured":"Chang CY, Lei YF, Tseng CH, Shih SR (2010) Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans Biomed Eng 57(6):1348\u20131357. https:\/\/doi.org\/10.1109\/TBME.2010.2041003","journal-title":"IEEE Trans Biomed Eng"},{"key":"10635_CR10","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. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105329","journal-title":"Comput Methods Programs Biomed"},{"key":"10635_CR11","doi-asserted-by":"publisher","first-page":"105906","DOI":"10.1016\/j.cmpb.2020.105906","volume":"199","author":"C Chu","year":"2021","unstructured":"Chu C, Zheng J, Zhou Y (2021) Ultrasonic thyroid nodule detection method based on U-net network. Comput Methods Progr Biomed 199:105906. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105906","journal-title":"Comput Methods Progr Biomed"},{"issue":"4","key":"10635_CR12","doi-asserted-by":"publisher","first-page":"794","DOI":"10.3390\/diagnostics12040794","volume":"12","author":"EF Cleere","year":"2022","unstructured":"Cleere EF, Davey MG, O\u2019Neill S, Corbett M, O\u2019Donnell JP, Hacking S et al (2022) Radiomic detection of malignancy within thyroid nodules using ultrasonography\u2014a systematic review and meta-analysis. Diagnostics 12(4):794. https:\/\/doi.org\/10.3390\/diagnostics12040794","journal-title":"Diagnostics"},{"key":"10635_CR13","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10635_CR14","unstructured":"Dutta A, Gupta A, Zissermann A (2016) VGG image annotator (VIA). http:\/\/www.robots.ox.ac.uk\/vgg\/software\/via, 2"},{"key":"10635_CR15","unstructured":"Eaton-Rosen Z, Bragman F, Ourselin S, Cardoso MJ (2018) Improving data augmentation for medical image segmentation"},{"key":"10635_CR16","doi-asserted-by":"crossref","unstructured":"Falc\u00f3-Gimeno A, Vallb\u00e9 JJ (2013) Coalition agreements and party preferences: a principal components analysis approach. In: EPSA 2013 annual general conference, paper 754","DOI":"10.2139\/ssrn.2241115"},{"key":"10635_CR17","doi-asserted-by":"publisher","unstructured":"Farrow E, Li J, Zaki F, Lall A (2020) Accessible streaming algorithms for the chi-square test. In: 32nd international conference on scientific and statistical database management, pp 1\u201312. https:\/\/doi.org\/10.1145\/3400903.3400905","DOI":"10.1145\/3400903.3400905"},{"key":"10635_CR18","unstructured":"Gaitini D, Evans RM, Ivanac G (2011) Chapter 16: thyroid ultrasound. EFSUMB Course Book"},{"key":"10635_CR19","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"10635_CR20","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1016\/j.matpr.2020.10.259","volume":"45","author":"MB Gulame","year":"2021","unstructured":"Gulame MB, Dixit VV, Suresh M (2021) Thyroid nodules segmentation methods in clinical ultrasound images: a review. Mater Today: Proc 45:2270\u20132276. https:\/\/doi.org\/10.1016\/j.matpr.2020.10.259","journal-title":"Mater Today: Proc"},{"issue":"1","key":"10635_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1089\/thy.2015.0020","volume":"26","author":"BR Haugen","year":"2016","unstructured":"Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE et al (2016) 2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association Guidelines Task Force on thyroid nodules and differentiated thyroid cancer. Thyroid 26(1):1\u2013133. https:\/\/doi.org\/10.1089\/thy.2015.0020","journal-title":"Thyroid"},{"key":"10635_CR23","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"issue":"1","key":"10635_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1210\/er.2002-0016","volume":"24","author":"L Hegedus","year":"2003","unstructured":"Hegedus L, Bonnema SJ, Bennedbaek FN (2003) Management of simple nodular goiter: current status and future perspectives. Endocr Rev 24(1):102\u2013132. https:\/\/doi.org\/10.1210\/er.2002-0016","journal-title":"Endocr Rev"},{"issue":"1","key":"10635_CR25","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3348\/kjr.2009.10.1.34","volume":"10","author":"TW Kang","year":"2009","unstructured":"Kang TW, Rhim H, Kim EY, Kim YS, Choi D, Lee WJ, Lim HK (2009) Percutaneous radiofrequency ablation for the hepatocellular carcinoma abutting the diaphragm: assessment of safety and therapeutic efficacy. Korean J Radiol 10(1):34\u201342. https:\/\/doi.org\/10.3348\/kjr.2009.10.1.34","journal-title":"Korean J Radiol"},{"key":"10635_CR26","doi-asserted-by":"crossref","unstructured":"Law H, Deng J (2018) Cornernet: detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision (ECCV), pp 734\u2013750","DOI":"10.1007\/978-3-030-01264-9_45"},{"issue":"1","key":"10635_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-25005-7","volume":"8","author":"H Li","year":"2018","unstructured":"Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y et al (2018) An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Sci Rep 8(1):1\u201312. https:\/\/doi.org\/10.1038\/s41598-018-25005-7","journal-title":"Sci Rep"},{"key":"10635_CR28","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"issue":"2","key":"10635_CR29","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX et al (2019) Deep learning in medical ultrasound analysis: a review. Engineering 5(2):261\u2013275. https:\/\/doi.org\/10.1016\/j.eng.2018.11.020","journal-title":"Engineering"},{"key":"10635_CR30","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"2","key":"10635_CR31","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1001\/archsurg.135.2.204","volume":"135","author":"CY Lo","year":"2000","unstructured":"Lo CY, Kwok KF, Yuen PW (2000) A prospective evaluation of recurrent laryngeal nerve paralysis during thyroidectomy. Arch Surg 135(2):204\u2013207. https:\/\/doi.org\/10.1001\/archsurg.135.2.204","journal-title":"Arch Surg"},{"issue":"4","key":"10635_CR32","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1109\/JBHI.2022.3153559","volume":"26","author":"J Lu","year":"2022","unstructured":"Lu J, Ouyang X, Shen X, Liu T, Cui Z, Wang Q, Shen D (2022) GAN-guided deformable attention network for identifying thyroid nodules in ultrasound images. IEEE J Biomed Health Inform 26(4):1582\u20131590. https:\/\/doi.org\/10.1109\/JBHI.2022.3153559","journal-title":"IEEE J Biomed Health Inform"},{"key":"10635_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3157828","author":"L Ma","year":"2022","unstructured":"Ma L, Tan G, Luo H, Liao Q, Li S, Li K (2022) A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2022.3157828","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"10635_CR34","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s12672-014-0205-y","volume":"6","author":"R Nagy","year":"2015","unstructured":"Nagy R, Ringel MD (2015) Genetic predisposition for nonmedullary thyroid cancer. Hormones Cancer 6(1):13\u201320. https:\/\/doi.org\/10.1007\/s12672-014-0205-y","journal-title":"Hormones Cancer"},{"issue":"4","key":"10635_CR35","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1002\/mp.15492","volume":"49","author":"L Pan","year":"2022","unstructured":"Pan L, Cai Y, Lin N, Yang L, Zheng S, Huang L (2022) A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images. Med Phys 49(4):2413\u20132426. https:\/\/doi.org\/10.1002\/mp.15492","journal-title":"Med Phys"},{"key":"10635_CR36","doi-asserted-by":"publisher","unstructured":"Pan H, Zhou Q, Latecki LJ (2021) SGUNET: semantic guided UNet for thyroid nodule segmentation. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI). IEEE, pp 630\u2013634. https:\/\/doi.org\/10.1109\/ISBI48211.2021.9434051","DOI":"10.1109\/ISBI48211.2021.9434051"},{"key":"10635_CR37","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra r-cnn: towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 821\u2013830","DOI":"10.1109\/CVPR.2019.00091"},{"key":"10635_CR38","doi-asserted-by":"crossref","unstructured":"Pedraza L, Vargas C, Narv\u00e1ez F, Dur\u00e1n O, Mu\u00f1oz E, Romero E (2015) An open access thyroid ultrasound image database. In: 10th international symposium on medical information processing and analysis. SPIE, vol 9287, pp 188\u2013193","DOI":"10.1117\/12.2073532"},{"issue":"4","key":"10635_CR39","doi-asserted-by":"publisher","first-page":"e250","DOI":"10.1016\/S2589-7500(21)00041-8","volume":"3","author":"S Peng","year":"2021","unstructured":"Peng S, Liu Y, Lv W, Liu L, Zhou Q, Yang H et al (2021) Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicenter diagnostic study. Lancet Digit Health 3(4):e250\u2013e259. https:\/\/doi.org\/10.1016\/S2589-7500(21)00041-8","journal-title":"Lancet Digit Health"},{"key":"10635_CR40","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"10635_CR41","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788. http:\/\/arxiv.org\/abs\/1506.02640","DOI":"10.1109\/CVPR.2016.91"},{"key":"10635_CR42","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Advances in neural information processing systems, p. 28"},{"issue":"5","key":"10635_CR43","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1159\/000478927","volume":"6","author":"G Russ","year":"2017","unstructured":"Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L (2017) European thyroid association guidelines for ultrasound malignancy risk stratification of thyroid nodules in adults: the EU-TIRADS. Eur Thyroid J 6(5):225\u2013237. https:\/\/doi.org\/10.1159\/000478927","journal-title":"Eur Thyroid J"},{"key":"10635_CR44","doi-asserted-by":"publisher","unstructured":"Shahroudnejad A, Qin X, Balachandran S, Dehghan M, Zonoobi D, Jaremko J et al (2021) TUN-det: a novel network for thyroid ultrasound nodule detection. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp. 656\u2013667. https:\/\/doi.org\/10.1007\/978-3-030-87193-2_62","DOI":"10.1007\/978-3-030-87193-2_62"},{"issue":"2","key":"10635_CR45","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.bbe.2021.02.008","volume":"41","author":"Y Sharifi","year":"2021","unstructured":"Sharifi Y, Bakhshali MA, Dehghani T, DanaiAshgzari M, Sargolzaei M, Eslami S (2021) Deep learning on ultrasound images of thyroid nodules. Biocybern Biomed Eng 41(2):636\u2013655. https:\/\/doi.org\/10.1016\/j.bbe.2021.02.008","journal-title":"Biocybern Biomed Eng"},{"issue":"1","key":"10635_CR46","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA: A Cancer J Clin 72(1):7\u201333. https:\/\/doi.org\/10.3322\/caac.21708","journal-title":"CA: A Cancer J Clin"},{"key":"10635_CR47","doi-asserted-by":"publisher","unstructured":"Singh S, Srivastava D, Agarwal S (2017) GLCM and its application in pattern recognition. In: 2017 5th international symposium on computational and business intelligence (ISCBI). IEEE, pp 20\u201325. https:\/\/doi.org\/10.1109\/ISCBI.2017.8053537","DOI":"10.1109\/ISCBI.2017.8053537"},{"issue":"10","key":"10635_CR48","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.7863\/ultra.15.14.10045","volume":"34","author":"G Song","year":"2015","unstructured":"Song G, Xue F, Zhang C (2015) A model using texture features to differentiate the nature of thyroid nodules on sonography. J Ultrasound Med 34(10):1753\u20131760. https:\/\/doi.org\/10.7863\/ultra.15.14.10045","journal-title":"J Ultrasound Med"},{"issue":"3","key":"10635_CR49","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/JBHI.2018.2852718","volume":"23","author":"W Song","year":"2018","unstructured":"Song W, Li S, Liu J, Qin H, Zhang B, Zhang S, Hao A (2018) Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Health Inform 23(3):1215\u20131224. https:\/\/doi.org\/10.1109\/JBHI.2018.2852718","journal-title":"IEEE J Biomed Health Inform"},{"key":"10635_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02967-2","author":"R Song","year":"2022","unstructured":"Song R, Zhu C, Zhang L, Zhang T, Luo Y, Liu J, Yang J (2022) Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02967-2","journal-title":"Appl Intell"},{"key":"10635_CR51","doi-asserted-by":"publisher","first-page":"106600","DOI":"10.1016\/j.cmpb.2021.106600","volume":"215","author":"J Sun","year":"2022","unstructured":"Sun J, Li C, Lu Z, He M, Zhao T, Li X et al (2022) TNSNet: thyroid nodule segmentation in ultrasound imaging using soft shape supervision. Comput Methods Progr Biomed 215:106600. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106600","journal-title":"Comput Methods Progr Biomed"},{"issue":"1","key":"10635_CR52","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiol.2017171240","volume":"287","author":"FN Tessler","year":"2018","unstructured":"Tessler FN, Middleton WD, Grant EG (2018) Thyroid imaging reporting and data system (TI-RADS): a user\u2019s guide. Radiology 287(1):29\u201336. https:\/\/doi.org\/10.1148\/radiol.2017171240","journal-title":"Radiology"},{"key":"10635_CR1","unstructured":"Thyroid for pretraining. https:\/\/www.kaggle.com\/tingzen\/thyroid-for-pretraining. Accessed 27 Apr 2022"},{"key":"10635_CR53","doi-asserted-by":"publisher","DOI":"10.3390\/books978-3-0365-3759-7","author":"P Trimboli","year":"2022","unstructured":"Trimboli P (2022) Risk stratification of thyroid nodule: from ultrasound features to TIRADS. MDPI. https:\/\/doi.org\/10.3390\/books978-3-0365-3759-7","journal-title":"MDPI"},{"issue":"2\u20133","key":"10635_CR54","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.cmpb.2006.09.006","volume":"84","author":"S Tsantis","year":"2006","unstructured":"Tsantis S, Dimitropoulos N, Cavouras D, Nikiforidis G (2006) A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images. Comput Methods Programs Biomed 84(2\u20133):86\u201398. https:\/\/doi.org\/10.1016\/j.cmpb.2006.09.006","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"10635_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12957-019-1558-z","volume":"17","author":"L Wang","year":"2019","unstructured":"Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y et al (2019) Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J Surg Oncol 17(1):1\u20139. https:\/\/doi.org\/10.1186\/s12957-019-1558-z","journal-title":"World J Surg Oncol"},{"issue":"10","key":"10635_CR56","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10635_CR58","doi-asserted-by":"publisher","unstructured":"Wu S, Zhu Q, Xie Y (2013) Evaluation of various speckle reduction filters on medical ultrasound images. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 1148\u20131151. https:\/\/doi.org\/10.1109\/EMBC.2013.6609709.","DOI":"10.1109\/EMBC.2013.6609709"},{"issue":"10","key":"10635_CR57","doi-asserted-by":"publisher","first-page":"3812","DOI":"10.1109\/JBHI.2021.3084962","volume":"25","author":"X Wu","year":"2021","unstructured":"Wu X, Tan G, Zhu N, Chen Z, Yang Y, Wen H, Li K (2021) CacheTrack-YOLO: real-time detection and tracking for thyroid nodules and surrounding tissues in ultrasound videos. IEEE J Biomed Health Inform 25(10):3812\u20133823. https:\/\/doi.org\/10.1109\/JBHI.2021.3084962","journal-title":"IEEE J Biomed Health Inform"},{"key":"10635_CR59","doi-asserted-by":"publisher","unstructured":"Xie S, Yu J, Liu T, Chang Q, Niu L, Sun W (2019) Thyroid nodule detection in ultrasound images with convolutional neural networks. In: 2019 14th IEEE conference on industrial electronics and applications (ICIEA). IEEE, pp 1442\u20131446. https:\/\/doi.org\/10.1109\/ICIEA.2019.8834375","DOI":"10.1109\/ICIEA.2019.8834375"},{"key":"10635_CR60","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. Med Image Anal 58:101552. https:\/\/doi.org\/10.1016\/j.media.2019.101552","journal-title":"Med Image Anal"},{"key":"10635_CR61","doi-asserted-by":"publisher","first-page":"105347","DOI":"10.1016\/j.compbiomed.2022.105347","volume":"144","author":"M Yu","year":"2022","unstructured":"Yu M, Han M, Li X, Wei X, Jiang H, Chen H, Yu R (2022) Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: thyroid ultrasound image case study. Comput Biol Med 144:105347. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105347","journal-title":"Comput Biol Med"},{"issue":"1","key":"10635_CR62","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3233\/XST-200775","volume":"29","author":"L Zhang","year":"2021","unstructured":"Zhang L, Zhuang Y, Hua Z, Han L, Li C, Chen K et al (2021) Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network. J X-Ray Sci Technol 29(1):75\u201390. https:\/\/doi.org\/10.3233\/XST-200775","journal-title":"J X-Ray Sci Technol"},{"issue":"1","key":"10635_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2047-2501-1-5","volume":"1","author":"J Zhao","year":"2013","unstructured":"Zhao J, Zheng W, Zhang L, Tian H (2013) Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology. Health Inf Sci Syst 1(1):1\u201312. https:\/\/doi.org\/10.1186\/2047-2501-1-5","journal-title":"Health Inf Sci Syst"},{"issue":"12","key":"10635_CR64","doi-asserted-by":"publisher","first-page":"7959","DOI":"10.1002\/mp.15319","volume":"48","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Yang C, Wang Q, Zhang H, Shi L, Zhang Z (2021) A Deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules. Med Phys 48(12):7959\u20137970. https:\/\/doi.org\/10.1002\/mp.15319","journal-title":"Med Phys"},{"key":"10635_CR65","doi-asserted-by":"publisher","first-page":"27917","DOI":"10.1109\/ACCESS.2022.3156096","volume":"10","author":"X Zhao","year":"2022","unstructured":"Zhao X, Shen X, Wan W, Lu Y, Hu S, Xiao R et al (2022) Automatic thyroid ultrasound image classification using feature fusion network. IEEE Access 10:27917\u201327924. https:\/\/doi.org\/10.1109\/ACCESS.2022.3156096","journal-title":"IEEE Access"},{"issue":"10","key":"10635_CR66","doi-asserted-by":"publisher","first-page":"13253","DOI":"10.1007\/s11042-021-10939-4","volume":"81","author":"Y Zheng","year":"2022","unstructured":"Zheng Y, Qin L, Qiu T, Zhou A, Xu P, Xue Z (2022b) Automated detection and recognition of thyroid nodules in ultrasound images using improve cascade mask R-CNN. Multimed Tools Appl 81(10):13253\u201313273","journal-title":"Multimed Tools Appl"},{"key":"10635_CR67","doi-asserted-by":"publisher","unstructured":"Zheng Z, Chen J, Weng Z, Zhang Y (2022) Comparison and analysis of ultrasound diagnosis networks for thyroid nodules based on different computer vision task types. In: 2022 3rd international conference on electronic communication and artificial intelligence (IWECAI), IEEE, pp 409\u2013413. https:\/\/doi.org\/10.1109\/IWECAI55315.2022.00086","DOI":"10.1109\/IWECAI55315.2022.00086"},{"key":"10635_CR68","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"10635_CR69","doi-asserted-by":"publisher","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159. https:\/\/doi.org\/10.48550\/arXiv.2010.04159","DOI":"10.48550\/arXiv.2010.04159"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10635-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10635-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10635-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T21:29:58Z","timestamp":1709933398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10635-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":68,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["10635"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10635-9","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"19 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"47"}}