{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T18:04:26Z","timestamp":1793729066448,"version":"4.6.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"name":"Fondo SEP-Cinvestav de Apoyo a la Investigaci\u00f3n 2018","award":["FidSC2018\/145"],"award-info":[{"award-number":["FidSC2018\/145"]}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1016\/j.compbiomed.2020.104036","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T08:40:50Z","timestamp":1602146450000},"page":"104036","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":96,"special_numbering":"C","title":["A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound"],"prefix":"10.1016","volume":"126","author":[{"given":"Wilfrido","family":"G\u00f3mez-Flores","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wagner","family":"Coelho de Albuquerque Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.compbiomed.2020.104036_bib1","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Canc."},{"issue":"3","key":"10.1016\/j.compbiomed.2020.104036_bib2","doi-asserted-by":"crossref","first-page":"292","DOI":"10.2214\/AJR.10.6049","article-title":"The relationship of mammographic density and age: implications for breast cancer screening","volume":"198","author":"Checka","year":"2012","journal-title":"Am. J. Roentgenol."},{"issue":"7","key":"10.1016\/j.compbiomed.2020.104036_bib3","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.ejca.2010.12.002","article-title":"Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1 year follow-up\u201d","volume":"47","author":"Corsetti","year":"2011","journal-title":"Eur. J. Canc."},{"issue":"2","key":"10.1016\/j.compbiomed.2020.104036_bib4","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2214\/AJR.13.12072","article-title":"Screening breast ultrasound: Past, present, and future","volume":"204","author":"Brem","year":"2015","journal-title":"Am. J. Roentgenol."},{"issue":"1","key":"10.1016\/j.compbiomed.2020.104036_bib5","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S1076-6332(99)80058-0","article-title":"Improving breast cancer diagnosis with computer-aided diagnosis","volume":"6","author":"Jiang","year":"1999","journal-title":"Acad. Radiol."},{"issue":"5","key":"10.1016\/j.compbiomed.2020.104036_bib6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/5992.877391","article-title":"Computer-aided diagnosis of breast lesions in medical images","volume":"2","author":"Giger","year":"2000","journal-title":"Comput. Sci. Eng."},{"issue":"1","key":"10.1016\/j.compbiomed.2020.104036_bib7","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1118\/1.3265959","article-title":"Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation","volume":"37","author":"G\u00f3mez","year":"2010","journal-title":"Med. Phys."},{"issue":"7","key":"10.1016\/j.compbiomed.2020.104036_bib8","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1016\/j.ultrasmedbio.2016.02.016","article-title":"New fully automated method for segmentation of breast lesions on ultrasound based on texture analysis","volume":"42","author":"G\u00f3mez-Flores","year":"2016","journal-title":"Ultrasound Med. Biol."},{"issue":"5","key":"10.1016\/j.compbiomed.2020.104036_bib9","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial intelligence in breast imaging","volume":"74","author":"Le","year":"2019","journal-title":"Clin. Radiol."},{"issue":"2","key":"10.1016\/j.compbiomed.2020.104036_bib10","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2214\/AJR.18.20392","article-title":"New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence","volume":"212","author":"Gao","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.compbiomed.2020.104036_bib11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/d41586-019-03822-8","article-title":"AI shows promise for breast cancer screening","volume":"577","author":"Pisano","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.compbiomed.2020.104036_bib12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.compbiomed.2020.104036_bib13","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.compbiomed.2020.104036_bib14","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.patcog.2018.02.012","article-title":"Automatic breast ultrasound image segmentation: A survey","volume":"79","author":"Xian","year":"2018","journal-title":"Pattern Recogn."},{"issue":"2","key":"10.1016\/j.compbiomed.2020.104036_bib15","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: A review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.compbiomed.2020.104036_bib16","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12530-019-09297-2","article-title":"Survey of deep learning in breast cancer image analysis","volume":"11","author":"Debelee","year":"2020","journal-title":"Evolving Systems"},{"key":"10.1016\/j.compbiomed.2020.104036_bib17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11517-017-1770-3","article-title":"Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images","volume":"56","author":"Liu","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.compbiomed.2020.104036_bib18","series-title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","author":"Garcia-Garcia","year":"2017"},{"key":"10.1016\/j.compbiomed.2020.104036_bib19","doi-asserted-by":"crossref","first-page":"101657","DOI":"10.1016\/j.media.2020.101657","article-title":"Segmentation of breast ultrasound image with semantic classification of superpixels","volume":"61","author":"Huang","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2020.104036_bib20","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","article-title":"Survey on semantic segmentation using deep learning techniques","volume":"338","author":"Lateef","year":"2019","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.compbiomed.2020.104036_bib21","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated breast ultrasound lesions detection using convolutional neural networks","volume":"22","author":"Yap","year":"2018","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"10","key":"10.1016\/j.compbiomed.2020.104036_bib22","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.compbiomed.2020.104036_bib23","series-title":"Ultrasonic Scattering in Biological Tissues","author":"Shung","year":"1993"},{"key":"10.1016\/j.compbiomed.2020.104036_bib24","series-title":"Rethinking Atrous Convolution for Semantic Image Segmentation","author":"Chen","year":"2017"},{"issue":"5","key":"10.1016\/j.compbiomed.2020.104036_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0195816","article-title":"Automated and real-time segmentation of suspicious breast masses using convolutional neural network","volume":"13","author":"Kumar","year":"2018","journal-title":"PloS One"},{"issue":"51","key":"10.1016\/j.compbiomed.2020.104036_bib26","article-title":"An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures","volume":"19","author":"Cao","year":"2019","journal-title":"BMC Med. Imag."},{"issue":"19","key":"10.1016\/j.compbiomed.2020.104036_bib27","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000015200","article-title":"Detection and classification the breast tumors using mask R-CNN on sonograms","volume":"98","author":"Chiao","year":"2019","journal-title":"Medicine"},{"issue":"1","key":"10.1016\/j.compbiomed.2020.104036_bib28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.6.1.011007","article-title":"Breast ultrasound lesions recognition: end-to-end deep learning approaches","volume":"6","author":"Yap","year":"2018","journal-title":"J. Med. Imag."},{"key":"10.1016\/j.compbiomed.2020.104036_bib29","series-title":"Breast Ultrasound Region of Interest Detection and Lesion Localisation","first-page":"101880","author":"Yap","year":"2020"},{"issue":"1","key":"10.1016\/j.compbiomed.2020.104036_bib30","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1002\/mp.13268","article-title":"Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model","volume":"46","author":"Hu","year":"2019","journal-title":"Med. Phys."},{"issue":"7","key":"10.1016\/j.compbiomed.2020.104036_bib31","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1166\/jmihi.2019.2752","article-title":"Combining a fully convolutional network and an active contour model for automatic 2d breast tumor segmentation from ultrasound images","volume":"9","author":"Fang","year":"2019","journal-title":"Journal of Medical Imaging and Health Informatics"},{"issue":"8","key":"10.1016\/j.compbiomed.2020.104036_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0221535","article-title":"An RDAU-NET model for lesion segmentation in breast ultrasound images","volume":"14","author":"Zhuang","year":"2019","journal-title":"PloS One"},{"key":"10.1016\/j.compbiomed.2020.104036_bib33","doi-asserted-by":"crossref","first-page":"6399","DOI":"10.1007\/s13369-020-04480-z","article-title":"RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks","volume":"45","author":"Negi","year":"2020","journal-title":"Arabian J. Sci. Eng."},{"issue":"23","key":"10.1016\/j.compbiomed.2020.104036_bib34","doi-asserted-by":"crossref","first-page":"235013","DOI":"10.1088\/1361-6560\/ab5093","article-title":"Computer-aided diagnosis system for breast ultrasound images using deep learning","volume":"64","author":"Tanaka","year":"2019","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"10.1016\/j.compbiomed.2020.104036_bib35","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TMI.2018.2872031","article-title":"Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images","volume":"38","author":"Shin","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"10.1016\/j.compbiomed.2020.104036_bib36","doi-asserted-by":"crossref","first-page":"839","DOI":"10.3233\/XST-190548","article-title":"New one-step model of breast tumor locating based on deep learning","volume":"27","author":"Tao","year":"2019","journal-title":"J. X Ray Sci. Technol."},{"key":"10.1016\/j.compbiomed.2020.104036_bib37","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11548-020-02158-3","article-title":"Two-stage ultrasound image segmentation using U-Net and test time augmentation","volume":"15","author":"Amiri","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"3","key":"10.1016\/j.compbiomed.2020.104036_bib38","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1166\/jmihi.2020.2914","article-title":"Dsunet: a new network structure for detection and segmentation of ultrasound breast lesions","volume":"10","author":"Cui","year":"2020","journal-title":"Journal of Medical Imaging and Health Informatics"},{"key":"10.1016\/j.compbiomed.2020.104036_bib39","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data in Brief"},{"issue":"11","key":"10.1016\/j.compbiomed.2020.104036_bib40","doi-asserted-by":"crossref","first-page":"6105","DOI":"10.1002\/mp.12538","article-title":"Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions","volume":"44","author":"Piotrzkowska-Wroblewska","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.compbiomed.2020.104036_bib41","series-title":"Pattern Recognition and Image Analysis","first-page":"41","article-title":"Impact of ultrasound image reconstruction method on breast lesion classification with deep learning","author":"Byra","year":"2019"},{"issue":"4","key":"10.1016\/j.compbiomed.2020.104036_bib42","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.compbiomed.2020.104036_bib43","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.compbiomed.2020.104036_bib44","series-title":"U-net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"issue":"12","key":"10.1016\/j.compbiomed.2020.104036_bib45","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compbiomed.2020.104036_bib46","series-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.compbiomed.2020.104036_bib47","series-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2015"},{"key":"10.1016\/j.compbiomed.2020.104036_bib48","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"Mobilenetv2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"10.1016\/j.compbiomed.2020.104036_bib49","series-title":"Xception: Deep Learning with Depthwise Separable Convolutions","author":"Chollet","year":"2016"},{"key":"10.1016\/j.compbiomed.2020.104036_bib50","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.compbiomed.2020.104036_bib51","series-title":"V-net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","author":"Milletari","year":"2016"},{"issue":"2","key":"10.1016\/j.compbiomed.2020.104036_bib52","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/2150704X.2016.1235299","article-title":"Satcnn: satellite image dataset classification using agile convolutional neural networks","volume":"8","author":"Zhong","year":"2017","journal-title":"Remote Sensing Letters"},{"issue":"60","key":"10.1016\/j.compbiomed.2020.104036_bib53","first-page":"1","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"Journal of Big Data"},{"key":"10.1016\/j.compbiomed.2020.104036_bib54","series-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"key":"10.1016\/j.compbiomed.2020.104036_bib55","series-title":"Applied Nonparametric Statistics Methods","author":"Sprent","year":"2001"},{"issue":"2","key":"10.1016\/j.compbiomed.2020.104036_bib56","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm","year":"1979","journal-title":"Scand. J. Stat."},{"issue":"3\u20134","key":"10.1016\/j.compbiomed.2020.104036_bib57","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality (complete samples)","volume":"52","author":"Shapiro","year":"1965","journal-title":"Biometrika"},{"key":"10.1016\/j.compbiomed.2020.104036_bib58","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogrammetry Remote Sens."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001048252030367X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001048252030367X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T19:27:54Z","timestamp":1757878074000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S001048252030367X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":58,"alternative-id":["S001048252030367X"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104036","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2020,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104036","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"104036"}}