{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:37:58Z","timestamp":1792784278222,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2022ZDZX0023"],"award-info":[{"award-number":["2022ZDZX0023"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007452","name":"Wu Jieping Medical Foundation","doi-asserted-by":"publisher","award":["HX-H2111282"],"award-info":[{"award-number":["HX-H2111282"]}],"id":[{"id":"10.13039\/100007452","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Achievement Conversion and Guidance Project of Chengdu Science and Technology Bureau","award":["2017-CY02-00027-GX"],"award-info":[{"award-number":["2017-CY02-00027-GX"]}],"id":[{"id":"https:\/\/ror.org\/00xfea068","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Interdiscip Sci Comput Life Sci"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s12539-022-00547-7","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T03:32:48Z","timestamp":1674099168000},"page":"262-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8607-091X","authenticated-orcid":false,"given":"Jin","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Jun","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing-Yan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Yue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi-Cheng","family":"Lao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Rong","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengling","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao-Liang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Lan","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"issue":"4","key":"547_CR1","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/msp.2019.2900993","volume":"36","author":"P Afshar","year":"2019","unstructured":"Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H (2019) From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process Mag 36(4):132\u2013160. https:\/\/doi.org\/10.1109\/msp.2019.2900993","journal-title":"IEEE Signal Process Mag"},{"issue":"1","key":"547_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trend Mach Learn 3(1):1\u2013122. https:\/\/doi.org\/10.1561\/2200000016","journal-title":"Found Trend Mach Learn"},{"issue":"2","key":"547_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/s0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(2):185\u2013205. https:\/\/doi.org\/10.1142\/s0219720005001004","journal-title":"J Bioinform Comput Biol"},{"issue":"2","key":"547_CR4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.clinimag.2019.12.013","volume":"60","author":"DB Erlichman","year":"2020","unstructured":"Erlichman DB, Weiss A, Koenigsberg M, Stein MW (2020) Contrast enhanced ultrasound: a review of radiology applications. Clin Imaging 60(2):209\u2013215. https:\/\/doi.org\/10.1016\/j.clinimag.2019.12.013","journal-title":"Clin Imaging"},{"key":"547_CR5","doi-asserted-by":"publisher","first-page":"109197","DOI":"10.1016\/j.ejrad.2020.109197","volume":"131","author":"EDFC Fleury","year":"2020","unstructured":"Fleury EDFC, Marcomini K (2020) Impact of radiomics on the breast ultrasound radiologist\u2019s clinical practice: from lumpologist to data wrangler. Europ J Radiol. 131:109197. https:\/\/doi.org\/10.1016\/j.ejrad.2020.109197","journal-title":"Europ J Radiol."},{"issue":"1","key":"547_CR6","first-page":"1","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Statis Software. 33(1):1\u201322","journal-title":"J Statis Software."},{"issue":"6","key":"547_CR7","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1007\/s11604-019-00831-5","volume":"37","author":"T Fujioka","year":"2019","unstructured":"Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kasahara M, Oda G, Ishiba T, Nakagawa T, Tateishi U (2019) Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol 37(6):466\u2013472. https:\/\/doi.org\/10.1007\/s11604-019-00831-5","journal-title":"Jpn J Radiol"},{"issue":"2","key":"547_CR8","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563\u2013577. https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"547_CR9","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill J-B, Strub F, Altch\u00e9 F, Tallec C, Richemond P, Buchatskaya E, Doersch C, Avila Pires B, Guo Z, Gheshlaghi Azar M (2020) Bootstrap your own latent-a new approach to self-supervised learning. Adv Neural Inf Process Syst 33:21271\u201321284","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"547_CR10","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.ejrad.2012.02.004","volume":"82","author":"R Gruber","year":"2013","unstructured":"Gruber R, Jaromi S, Rudas M, Pfarl G, Riedl CC, Flory D, Graf O, Sickles EA, Helbich TH (2013) Histologic work-up of non-palpable breast lesions classified as probably benign at initial mammography and\/or ultrasound (BI-RADS category 3). Eur J Radiol 82(3):398\u2013403. https:\/\/doi.org\/10.1016\/j.ejrad.2012.02.004","journal-title":"Eur J Radiol"},{"issue":"8","key":"547_CR11","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"P Hanchuan","year":"2005","unstructured":"Hanchuan P, Fuhui L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238. https:\/\/doi.org\/10.1109\/TPAMI.2005.159","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"547_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. IEEE Conf Compt Vision Patt Recogn. https:\/\/doi.org\/10.1109\/CVPR.2016.90","journal-title":"IEEE Conf Compt Vision Patt Recogn"},{"key":"547_CR13","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 05 MB model size. arXiv preprint arXiv. 1602.07360."},{"issue":"6","key":"547_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"547_CR15","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10396-021-01091-5","volume":"48","author":"K Kubota","year":"2021","unstructured":"Kubota K (2021) Breast ultrasound in the age of advanced technology and artificial intelligence. J Med Ultrason 48(2):113\u2013114. https:\/\/doi.org\/10.1007\/s10396-021-01091-5","journal-title":"J Med Ultrason"},{"issue":"12","key":"547_CR16","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, Van Wijk Y, Woodruff H, Van Soest J, Lustberg T, Roelofs E, Van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749\u2013762. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","journal-title":"Nat Rev Clin Oncol"},{"issue":"1","key":"547_CR17","doi-asserted-by":"publisher","first-page":"13546","DOI":"10.1038\/s41598-018-31906-4","volume":"8","author":"SE Lee","year":"2018","unstructured":"Lee SE, Han K, Kwak JY, Lee E, Kim E-K (2018) Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 8(1):13546. https:\/\/doi.org\/10.1038\/s41598-018-31906-4","journal-title":"Sci Rep"},{"issue":"1","key":"547_CR18","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1002\/jum.15069","volume":"39","author":"W Li","year":"2020","unstructured":"Li W, Zhou Q, Xia S, Wu Y, Fei X, Wang Y, Tao L, Fan J, Zhou W (2020) Application of contrast-enhanced ultrasound in the diagnosis of ductal carcinoma in situ: analysis of 127 cases. J Ultrasound Med 39(1):39\u201350. https:\/\/doi.org\/10.1002\/jum.15069","journal-title":"J Ultrasound Med"},{"key":"547_CR19","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio Aa A, Ciompi F, Ghafoorian M, Van Der Laak Ja WM, Van Ginneken B, Sanchez CI (2017) A survey on deep learning in medical image analysis. Med Image Analy. 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Analy."},{"key":"547_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866","author":"X Liu","year":"2021","unstructured":"Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J (2021) Self-supervised learning: generative or contrastive. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2021.3090866","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"547_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14020277","author":"ZA Magnuska","year":"2022","unstructured":"Magnuska ZA, Theek B, Darguzyte M, Palmowski M, Stickeler E, Schulz V, Kiessling F (2022) Influence of the computer-aided decision support system design on ultrasound-based breast cancer classification. Cancers. https:\/\/doi.org\/10.3390\/cancers14020277","journal-title":"Cancers"},{"issue":"6","key":"547_CR22","doi-asserted-by":"publisher","first-page":"3024","DOI":"10.1118\/1.4921123","volume":"42","author":"WK Moon","year":"2015","unstructured":"Moon WK, Huang Y-S, Lo C-M, Huang C-S, Bae MS, Kim WH, Chen J-H, Chang R-F (2015) Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. Med Phys 42(6):3024\u20133035. https:\/\/doi.org\/10.1118\/1.4921123","journal-title":"Med Phys"},{"issue":"1","key":"547_CR23","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1002\/jum.15684","volume":"41","author":"AM O'connell","year":"2022","unstructured":"O\u2019connell AM, Bartolotta TV, Orlando A, Jung S-H, Baek J, Parker KJ (2022) Diagnostic performance of an artificial intelligence system in breast ultrasound. J Ultrasound Med 41(1):97\u2013105. https:\/\/doi.org\/10.1002\/jum.15684","journal-title":"J Ultrasound Med"},{"issue":"5","key":"547_CR24","doi-asserted-by":"publisher","first-page":"103046","DOI":"10.1016\/j.ipm.2022.103046","volume":"59","author":"J-J Qiu","year":"2022","unstructured":"Qiu J-J, Yin J, Ji L, Lu C-Y, Li K, Zhang Y-G, Lin Y-XJIP (2022) Management Differential diagnosis of hepatocellular carcinoma and hepatic hemangioma based on maximum wavelet-coefficient statistics: novel radiomics features from plain CT. Inform Process Manag. 59(5):103046","journal-title":"Inform Process Manag."},{"issue":"1","key":"547_CR25","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/TMI.2020.3021254","volume":"40","author":"J-J Qiu","year":"2021","unstructured":"Qiu J-J, Yin J, Qian W, Liu J-H, Huang Z-X, Yu H-P, Ji L, Zeng X-X (2021) A novel multiresolution-statistical texture analysis architecture radiomics-aided diagnosis of pdac based on plain CT images. IEEE Trans Med Imaging 40(1):12\u201325. https:\/\/doi.org\/10.1109\/TMI.2020.3021254","journal-title":"IEEE Trans Med Imaging"},{"key":"547_CR26","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv. 1804.02767."},{"key":"547_CR27","doi-asserted-by":"crossref","unstructured":"Roffo G, Melzi S, Castellani U, Vinciarelli A (2017) Infinite latent feature selection: a probabilistic latent graph-based ranking approach. International Conference on Computer Vision. Venice, Italy.","DOI":"10.1109\/ICCV.2017.156"},{"issue":"3","key":"547_CR28","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vision"},{"key":"547_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk H-I (2017) Deep Learning in medical image analysis. Annu Rev Biomed Eng 19:221\u2013248. https:\/\/doi.org\/10.1146\/annurev-bioeng-071516-044442","journal-title":"Annu Rev Biomed Eng"},{"issue":"1","key":"547_CR30","doi-asserted-by":"publisher","first-page":"5645","DOI":"10.1038\/s41467-021-26023-2","volume":"12","author":"Y Shen","year":"2021","unstructured":"Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, Wu N, Huddleston C, Wolfson S, Millet A, Ehrenpreis R, Awal D, Tyma C, Samreen N, Gao Y, Chhor C, Gandhi S, Lee C, Kumari-Subaiya S, Leonard C, Mohammed R, Moczulski C, Altabet J, Babb J, Lewin A, Reig B, Moy L, Heacock L, Geras KJ (2021) Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 12(1):5645. https:\/\/doi.org\/10.1038\/s41467-021-26023-2","journal-title":"Nat Commun"},{"key":"547_CR31","doi-asserted-by":"publisher","first-page":"101829","DOI":"10.1016\/j.compmedimag.2020.101829","volume":"87","author":"W-C Shia","year":"2021","unstructured":"Shia W-C, Chen D-R (2021) Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine. Compt Med Imag Graph. 87:101829. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101829","journal-title":"Compt Med Imag Graph."},{"issue":"5","key":"547_CR32","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.ultrasmedbio.2020.01.015","volume":"46","author":"Y Sim","year":"2020","unstructured":"Sim Y, Lee SE, Kim E-K, Kim S (2020) A radiomics approach for the classification of fibroepithelial lesions on breast ultrasonography. Ultrasound Med Biol 46(5):1133\u20131141. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2020.01.015","journal-title":"Ultrasound Med Biol"},{"issue":"2","key":"547_CR33","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1159\/000350774","volume":"8","author":"H-P Sinn","year":"2013","unstructured":"Sinn H-P, Kreipe H (2013) A brief overview of the who classification of breast tumors, 4th edition, focusing on issues and updates from the 3rd edition. Breast Care. 8(2):149\u2013154. https:\/\/doi.org\/10.1159\/000350774","journal-title":"Breast Care."},{"key":"547_CR34","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.nima.2012.09.006","volume":"702","author":"M Strzelecki","year":"2013","unstructured":"Strzelecki M, Szczypinski P, Materka A, Klepaczko A (2013) A software tool for automatic classification and segmentation of 2D\/3D medical images. Nucl Instrum Methods Phys Res, Sect A 702:137\u2013140. https:\/\/doi.org\/10.1016\/j.nima.2012.09.006","journal-title":"Nucl Instrum Methods Phys Res, Sect A"},{"key":"547_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2016.308"},{"key":"547_CR36","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning."},{"issue":"1","key":"547_CR37","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58(1):267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"21","key":"547_CR38","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM Van Griethuysen","year":"2017","unstructured":"Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL (2017) Computational radiomics system to decode the radiographic phenotype. Can Res 77(21):e104\u2013e107. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339","journal-title":"Can Res"},{"key":"547_CR39","doi-asserted-by":"publisher","first-page":"109624","DOI":"10.1016\/j.ejrad.2021.109624","volume":"138","author":"X-Y Wang","year":"2021","unstructured":"Wang X-Y, Cui L-G, Feng J, Chen W (2021) Artificial intelligence for breast ultrasound: an adjunct tool to reduce excessive lesion biopsy. Europ J Radiol. 138:109624. https:\/\/doi.org\/10.1016\/j.ejrad.2021.109624","journal-title":"Europ J Radiol."},{"key":"547_CR40","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.2147\/CMAR.S359986","volume":"14","author":"M Xu","year":"2022","unstructured":"Xu M, Li F, Yu S, Zeng S, Weng G, Teng P, Yang H, Li X, Liu G (2022) Value of histogram of gray-scale ultrasound image in differential diagnosis of small triple negative breast invasive ductal carcinoma and fibroadenoma. Cancer Manage Res 14:1515\u20131524. https:\/\/doi.org\/10.2147\/CMAR.S359986","journal-title":"Cancer Manage Res"},{"issue":"4\u20135","key":"547_CR41","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1177\/0161734620928453","volume":"42","author":"X Zhang","year":"2020","unstructured":"Zhang X, Lin X, Zhang Z, Dong L, Sun X, Sun D, Yuan K (2020) Artificial intelligence medical ultrasound equipment: application of breast lesions detection. Ultrason Imaging 42(4\u20135):191\u2013202. https:\/\/doi.org\/10.1177\/0161734620928453","journal-title":"Ultrason Imaging"}],"container-title":["Interdisciplinary Sciences: Computational Life Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-022-00547-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12539-022-00547-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-022-00547-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T11:20:06Z","timestamp":1682767206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12539-022-00547-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["547"],"URL":"https:\/\/doi.org\/10.1007\/s12539-022-00547-7","relation":{},"ISSN":["1913-2751","1867-1462"],"issn-type":[{"value":"1913-2751","type":"print"},{"value":"1867-1462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]},"assertion":[{"value":"5 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}