乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:10:11Z","timestamp":1744157411854,"version":"3.40.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014440","name":"ministerio de ciencia, innovaci\u00f3n y universidades","doi-asserted-by":"publisher","award":["RTI2018-094645-B"],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11517-021-02497-6","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T13:04:19Z","timestamp":1646312659000},"page":"1159-1175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-4388","authenticated-orcid":false,"given":"Saul","family":"Calderon-Ramirez","sequence":"first","affiliation":[]},{"given":"Diego","family":"Murillo-Hernandez","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Rojas-Salazar","sequence":"additional","affiliation":[]},{"given":"David","family":"Elizondo","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Armaghan","family":"Moemeni","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Molina-Cabello","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"issue":"11","key":"2497_CR1","first-page":"1","volume":"20","author":"D Abdelhafiz","year":"2019","unstructured":"Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinforma 20(11):1\u201320","journal-title":"BMC Bioinforma"},{"key":"2497_CR2","unstructured":"Akosa J (2017) Predictive accuracy: A misleading performance measure for highly imbalanced data. In: Proceedings of the SAS global forum, vol 12"},{"key":"2497_CR3","doi-asserted-by":"crossref","unstructured":"Alfaro E, Fonseca XB, Albornoz EM, Mart\u00ednez CE, Ramrez SC (2019) A brief analysis of u-net and mask r-cnn for skin lesion segmentation. In: 2019 IEEE international work conference on bioinspired intelligence (IWOBI). IEEE, pp 000123\u2013000126","DOI":"10.1109\/IWOBI47054.2019.9114436"},{"issue":"11","key":"2497_CR4","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.3390\/app10113999","volume":"10","author":"M Alkhaleefah","year":"2020","unstructured":"Alkhaleefah M, Ma SC, Chang YL, Huang B, Chittem PK, Achhannagari VP (2020) Double-shot transfer learning for breast cancer classification from x-ray images. Appl Sci 10(11):3999","journal-title":"Appl Sci"},{"key":"2497_CR5","unstructured":"American Cancer Society (2019) Breast cancer facts & figures 2019-2020. American Cancer Society, Inc. pp 1\u201344"},{"key":"2497_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2020.09.037","volume":"421","author":"R Bakalo","year":"2021","unstructured":"Bakalo R, Goldberger J, Ben-Ari R (2021) Weakly and semi supervised detection in medical imaging via deep dual branch net. Neurocomputing 421:15\u201325. https:\/\/doi.org\/10.1016\/j.neucom.2020.09.037","journal-title":"Neurocomputing"},{"issue":"4","key":"2497_CR7","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.carj.2019.06.002","volume":"70","author":"I Balki","year":"2019","unstructured":"Balki I, Amirabadi A, Levman J, Martel AL, Emersic Z, Meden B, Garcia-Pedrero A, Ramirez SC, Kong D, Moody AR et al (2019) Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Can Assoc Radiol J 70(4):344\u2013353","journal-title":"Can Assoc Radiol J"},{"key":"2497_CR8","doi-asserted-by":"crossref","unstructured":"Beeravolu AR, Azam S, Jonkman M, Shanmugam B, Kannoorpatti K, Anwar A (2021) Preprocessing of breast cancer images to create datasets for deep-cnn. IEEE Access","DOI":"10.1109\/ACCESS.2021.3058773"},{"key":"2497_CR9","doi-asserted-by":"crossref","unstructured":"Bermudez A, Calderon-Ramirez S, Thang T, Tyrrell P, Moemeni A, Yang S, Torrents-Barrena J (2020) A first glance to the quality assessment of dental photostimulable phosphor plates with deep learning. In: 2020 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20136","DOI":"10.1109\/IJCNN48605.2020.9206779"},{"issue":"1","key":"2497_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/bib\/bbr008","volume":"13","author":"D Berrar","year":"2012","unstructured":"Berrar D, Flach P (2012) Caveats and pitfalls of roc analysis in clinical microarray research (and how to avoid them). Brief Bioinforma 13(1):83\u201397","journal-title":"Brief Bioinforma"},{"key":"2497_CR11","unstructured":"Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel CA (2019) Mixmatch: A holistic approach to semi-supervised learning. In: Advances in neural information processing systems, pp 5049\u20135059"},{"key":"2497_CR12","doi-asserted-by":"crossref","unstructured":"Calderon-Ramirez S, Fallas F, Zumbado M, Tyrrell PN, Stark H, Emersic Z, Meden B, Solis M (2018) Assessing the impact of the deceived non local means filter as a preprocessing stage in a convolutional neural network based approach for age estimation using digital hand x-ray images. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 1752\u20131756","DOI":"10.1109\/ICIP.2018.8451191"},{"key":"2497_CR13","doi-asserted-by":"crossref","unstructured":"Calderon-Ramirez S, Giri R, Yang S, Moemeni A, Umana M, Elizondo D, Torrents-Barrena J, Molina-Cabello MA (2021) Dealing with scarce labelled data: Semi-supervised deep learning with mix match for covid-19 detection using chest x-ray images. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 5294\u20135301","DOI":"10.1109\/ICPR48806.2021.9412946"},{"key":"2497_CR14","doi-asserted-by":"crossref","unstructured":"Calderon-Ramirez S, Murillo-Hernandez D, Rojas-Salazar K, Calvo-Valverde LA, Yang S, Moemeni A, Elizondo D, Lopez-Rubio E, Molina-Cabello M (2021) Improving uncertainty estimations for mammogram classification using semi-supervised learning. In: Institute of electrical and electronics engineers","DOI":"10.1109\/IJCNN52387.2021.9533719"},{"key":"2497_CR15","unstructured":"Calderon-Ramirez S, Oala L (2021) More than meets the eye: Semi-supervised learning under non-iid data. arXiv:2104.10223"},{"key":"2497_CR16","unstructured":"Calderon-Ramirez S, Oala L, Torrents-Barrena J, Yang S, Moemeni A, Samek W, Molina-Cabello MA (2020) Mixmood: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures. arXiv:2006.07767"},{"key":"2497_CR17","doi-asserted-by":"crossref","unstructured":"Calderon-Ramirez S, Shengxiang-Yang, Moemeni A, Elizondo D, Colreavy-Donnelly S, Chavarria-Estrada LF, Molina-Cabello MA (2020) Correcting data imbalance for semi-supervised covid-19 detection using x-ray chest images","DOI":"10.1016\/j.asoc.2021.107692"},{"key":"2497_CR18","doi-asserted-by":"crossref","unstructured":"Calvo I, Calderon-Ramirez S, Torrents-Barrena J, Mu\u00f1oz E, Puig D (2019) Assessing the impact of a preprocessing stage on deep learning architectures for breast tumor multi-class classification with histopathological images. In: Latin american high performance computing conference. Springer, pp 262\u2013275","DOI":"10.1007\/978-3-030-41005-6_18"},{"key":"2497_CR19","doi-asserted-by":"crossref","unstructured":"Castro E, Cardoso JS, Pereira JC (2018) Elastic deformations for data augmentation in breast cancer mass detection. In: 2018 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE, pp 230\u2013234","DOI":"10.1109\/BHI.2018.8333411"},{"key":"2497_CR20","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280\u2013296. https:\/\/doi.org\/10.1016\/j.media.2019.03.009","journal-title":"Med Image Anal"},{"issue":"1","key":"2497_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):1\u201313","journal-title":"BMC Genomics"},{"issue":"6","key":"2497_CR22","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M et al (2013) The cancer imaging archive (tcia): Maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J Digit Imaging"},{"key":"2497_CR23","doi-asserted-by":"publisher","first-page":"112866","DOI":"10.1016\/j.eswa.2019.112866","volume":"140","author":"D Devarriya","year":"2020","unstructured":"Devarriya D, Gulati C, Mansharamani V, Sakalle A, Bhardwaj A (2020) Unbalanced breast cancer data classification using novel fitness functions in genetic programming. Expert Syst Appl 140:112866","journal-title":"Expert Syst Appl"},{"key":"2497_CR24","doi-asserted-by":"crossref","unstructured":"Dhungel N, Carneiro G, Bradley AP (2015) Deep learning and structured prediction for the segmentation of mass in mammograms. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 605\u2013612","DOI":"10.1007\/978-3-319-24553-9_74"},{"key":"2497_CR25","doi-asserted-by":"crossref","unstructured":"Domingues I, Abreu PH, Santos J (2018) Bi-rads classification of breast cancer: a new pre-processing pipeline for deep models training. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 1378\u20131382","DOI":"10.1109\/ICIP.2018.8451510"},{"key":"2497_CR26","doi-asserted-by":"crossref","unstructured":"Falcon\u00ed L, P\u00e9rez M, Aguilar W, Conci A (2020) Transfer learning and fine tuning in mammogram bi-rads classification. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS). IEEE, pp 475\u2013480","DOI":"10.1109\/CBMS49503.2020.00096"},{"issue":"1","key":"2497_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/1882471.1882479","volume":"12","author":"G Forman","year":"2010","unstructured":"Forman G, Scholz M (2010) Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. ACM Sigkdd Explor Newsl 12(1):49\u201357","journal-title":"ACM Sigkdd Explor Newsl"},{"key":"2497_CR28","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"key":"2497_CR29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.media.2018.03.006","volume":"47","author":"A Hamidinekoo","year":"2018","unstructured":"Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45\u201367","journal-title":"Med Image Anal"},{"issue":"1","key":"2497_CR30","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10994-009-5119-5","volume":"77","author":"DJ Hand","year":"2009","unstructured":"Hand DJ (2009) Measuring classifier performance: a coherent alternative to the area under the roc curve. Mach Learn 77(1):103\u2013123","journal-title":"Mach Learn"},{"key":"2497_CR31","doi-asserted-by":"publisher","unstructured":"Heath M., Bowyer K., Kopans D., Kegelmeyer P., Moore R., Chang K., Munishkumaran S (1998) Current status of the digital database for screening mammography. In: Digital mammography. https:\/\/doi.org\/10.1007\/978-94-011-5318-8_75. Springer, pp 457\u2013460","DOI":"10.1007\/978-94-011-5318-8_75"},{"key":"2497_CR32","unstructured":"Hendrycks D, Dietterich T (2019) Benchmarking neural network robustness to common corruptions and perturbations"},{"key":"2497_CR33","doi-asserted-by":"crossref","unstructured":"Johnson JM, Khoshgoftaar TM (2019) Deep learning and thresholding with class-imbalanced big data. In: 2019 18th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 755\u2013762","DOI":"10.1109\/ICMLA.2019.00134"},{"key":"2497_CR34","unstructured":"Korkinof D, Rijken T, O\u2019Neill M, Yearsley J, Harvey H, Glocker B (2019) High-resolution mammogram synthesis using progressive generative adversarial networks"},{"key":"2497_CR35","unstructured":"Kubat M, Matwin S et al (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol 97. Citeseer, pp 179\u2013186"},{"key":"2497_CR36","doi-asserted-by":"publisher","unstructured":"Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4(1). https:\/\/doi.org\/10.1038\/sdata.2017.177","DOI":"10.1038\/sdata.2017.177"},{"key":"2497_CR37","doi-asserted-by":"publisher","unstructured":"Lee RS, Gimenez F, Hoogi A, Rubin D (2016) Curated breast imaging subset of ddsm The cancer imaging archive. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.7O02S9CY","DOI":"10.7937\/K9\/TCIA.2016.7O02S9CY"},{"key":"2497_CR38","unstructured":"L\u00e9vy D, Jain A (2016) Breast mass classification from mammograms using deep convolutional neural networks. arXiv:1612.00542"},{"key":"2497_CR39","doi-asserted-by":"crossref","unstructured":"Li G, Xu S, Liu X, Li L, Wang C (2018) Jersey number recognition with semi-supervised spatial transformer network. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1783\u20131790","DOI":"10.1109\/CVPRW.2018.00231"},{"key":"2497_CR40","doi-asserted-by":"crossref","unstructured":"Li H, Chen D, Nailon WH, Davies ME, Laurenson DI (2019) Signed laplacian deep learning with adversarial augmentation for improved mammography diagnosis. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 486\u2013494","DOI":"10.1007\/978-3-030-32226-7_54"},{"key":"2497_CR41","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.ins.2013.04.016","volume":"257","author":"A Maratea","year":"2014","unstructured":"Maratea A, Petrosino A, Manzo M (2014) Adjusted f-measure and kernel scaling for imbalanced data learning. Inform Sci 257:331\u2013341","journal-title":"Inform Sci"},{"key":"2497_CR42","doi-asserted-by":"crossref","unstructured":"Mendez M, Calderon S, Tyrrell PN (2019) Using cluster analysis to assess the impact of dataset heterogeneity on deep convolutional network accuracy: A first glance. In: Latin American high performance computing conference. Springer, pp 307\u2013319","DOI":"10.1007\/978-3-030-41005-6_21"},{"key":"2497_CR43","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"I Moreira","year":"2011","unstructured":"Moreira I, Amaral I, Domingues I, Cardoso A, Cardoso M, Cardoso J (2011) Inbreast: Toward a full-field digital mammographic database. Acad Radiol 19:236\u201348. https:\/\/doi.org\/10.1016\/j.acra.2011.09.014","journal-title":"Acad Radiol"},{"issue":"7","key":"2497_CR44","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1007\/s11517-015-1411-7","volume":"54","author":"M Mustra","year":"2016","unstructured":"Mustra M, Grgic M, Rangayyan RM (2016) Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput 54(7):1003\u2013 1024","journal-title":"Med Biol Eng Comput"},{"key":"2497_CR45","unstructured":"Oala L, Fehr J, Gilli L, Balachandran P, Leite AW, Calderon-Ramirez S, Li DX, Nobis G, Alvarado EAM, Jaramillo-Gutierrez G et al (2020) Ml4h auditing: From paper to practice. In: Machine learning for health. PMLR, pp 280\u2013317"},{"key":"2497_CR46","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.procs.2018.08.190","volume":"135","author":"B Pardamean","year":"2018","unstructured":"Pardamean B, Cenggoro TW, Rahutomo R, Budiarto A, Karuppiah EK (2018) Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data. Procedia Comput Sci 135:400\u2013407. https:\/\/doi.org\/10.1016\/j.procs.2018.08.190. The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018) : Empowering Smart Technology in Digital Era for a Better Life","journal-title":"Procedia Comput Sci"},{"key":"2497_CR47","unstructured":"Powers DM (2015) What the f-measure doesn\u2019t measure: Features, flaws, fallacies and fixes. arXiv:1503.06410"},{"issue":"1","key":"2497_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W (2019) Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9(1):1\u201312","journal-title":"Sci Rep"},{"key":"2497_CR49","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2020.01.099","volume":"393","author":"R Shen","year":"2020","unstructured":"Shen R, Yao J, Yan K, Tian K, Jiang C, Zhou K (2020) Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing 393:27\u201337","journal-title":"Neurocomputing"},{"key":"2497_CR50","doi-asserted-by":"crossref","unstructured":"Shi Q, Zhang H (2020) Fault diagnosis of an autonomous vehicle with an improved svm algorithm subject to unbalanced datasets. IEEE Trans Ind Electron","DOI":"10.1109\/TIE.2020.2994868"},{"key":"2497_CR51","doi-asserted-by":"crossref","unstructured":"Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence. Springer, pp 1015\u20131021","DOI":"10.1007\/11941439_114"},{"key":"2497_CR52","doi-asserted-by":"publisher","unstructured":"Sun L, Wen J, Wang J, Zhao Y, Xu Y (2020) Classification of mammography based on semi-supervised learning. In: 2020 IEEE international conference on progress in informatics and computing (PIC). https:\/\/doi.org\/10.1109\/PIC50277.2020.9350835, pp 104\u2013111","DOI":"10.1109\/PIC50277.2020.9350835"},{"key":"2497_CR53","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.compmedimag.2016.07.004","volume":"57","author":"W Sun","year":"2017","unstructured":"Sun W, Tseng TLB, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graphics 57:4\u20139. https:\/\/doi.org\/10.1016\/j.compmedimag.2016.07.004. Recent Developments in Machine Learning for Medical Imaging Applications","journal-title":"Comput Med Imaging Graphics"},{"key":"2497_CR54","doi-asserted-by":"publisher","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2017) On the necessity of fine-tuned convolutional neural networks for medical imaging. In: Deep learning and convolutional neural networks for medical image computing. https:\/\/doi.org\/10.1007\/978-3-319-42999-1_11. Springer, pp 181\u2013193","DOI":"10.1007\/978-3-319-42999-1_11"},{"key":"2497_CR55","doi-asserted-by":"crossref","unstructured":"Tardy M, Mateus D (2021) Looking for abnormalities in mammograms with self-and weakly supervised reconstruction. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2021.3050040"},{"key":"2497_CR56","doi-asserted-by":"publisher","unstructured":"Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy PJ (2016) Training deep neural networks on imbalanced data sets. In: 2016 international joint conference on neural networks (IJCNN). https:\/\/doi.org\/10.1109\/IJCNN.2016.7727770, pp 4368\u20134374","DOI":"10.1109\/IJCNN.2016.7727770"},{"key":"2497_CR57","unstructured":"Wild C, Weiderpass E, Stewart B (2020) World cancer report: cancer research for cancer prevention. Lyon: International Agency for Research on Cancer"},{"key":"2497_CR58","unstructured":"Wu E, Wu K, Lotter W (2020) Synthesizing lesions using contextual gans improves breast cancer classification on mammograms"},{"key":"2497_CR59","doi-asserted-by":"publisher","first-page":"15844","DOI":"10.1109\/ACCESS.2018.2810849","volume":"6","author":"Q Zheng","year":"2018","unstructured":"Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep cnn via implicit regularization in two-stage training process. IEEE Access 6:15844\u201315869","journal-title":"IEEE Access"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02497-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-021-02497-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02497-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:30:28Z","timestamp":1744155028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-021-02497-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,3]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["2497"],"URL":"https:\/\/doi.org\/10.1007\/s11517-021-02497-6","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2022,3,3]]},"assertion":[{"value":"1 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}