{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:34:45Z","timestamp":1782779685544,"version":"3.54.5"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2020,6,13]],"date-time":"2020-06-13T00:00:00Z","timestamp":1592006400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001796","name":"Gustavus and Louise Pfeiffer Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001796","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1016\/j.bspc.2020.102027","type":"journal-article","created":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T17:26:22Z","timestamp":1593192382000},"page":"102027","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":291,"special_numbering":"C","title":["Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network"],"prefix":"10.1016","volume":"61","author":[{"given":"Michal","family":"Byra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Jarosik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Szubert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Galperin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haydee","family":"Ojeda-Fournier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda","family":"Olson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"O\u2019Boyle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Comstock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Andre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2020.102027_bib0005","first-page":"394","article-title":"Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA: Cancer J. Clin."},{"key":"10.1016\/j.bspc.2020.102027_bib0010","doi-asserted-by":"crossref","first-page":"19","DOI":"10.4329\/wjr.v11.i2.19","article-title":"Artificial intelligence in breast ultrasound","volume":"11","author":"Wu","year":"2019","journal-title":"World J. Radiol."},{"key":"10.1016\/j.bspc.2020.102027_bib0015","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1109\/TMI.2006.877092","article-title":"Ultrasound image segmentation: a survey","volume":"25","author":"Noble","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2020.102027_bib0020","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."},{"key":"10.1016\/j.bspc.2020.102027_bib0025","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2020.102027_bib0030","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.1002\/mp.12453","article-title":"A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets","volume":"44","author":"Antropova","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2020.102027_bib0035","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":"2017","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.bspc.2020.102027_bib0040","doi-asserted-by":"crossref","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","article-title":"A deep learning framework for supporting the classification of breast lesions in ultrasound images","volume":"62","author":"Han","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.bspc.2020.102027_bib0045","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.bbe.2018.05.003","article-title":"Discriminant analysis of neural style representations for breast lesion classification in ultrasound","volume":"38","author":"Byra","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.bspc.2020.102027_bib0050","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. Imaging"},{"key":"10.1016\/j.bspc.2020.102027_bib0055","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1002\/mp.13361","article-title":"Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion","volume":"46","author":"Byra","year":"2019","journal-title":"Medi. Phys."},{"key":"10.1016\/j.bspc.2020.102027_bib0060","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.media.2018.12.006","article-title":"Automated diagnosis of breast ultrasonography images using deep neural networks","volume":"52","author":"Qi","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2020.102027_bib0065","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."},{"key":"10.1016\/j.bspc.2020.102027_bib0070","first-page":"105275","article-title":"Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network","author":"Han","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2020.102027_bib0075","first-page":"234","article-title":"U-net. Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"10.1016\/j.bspc.2020.102027_bib0080","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-net: deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"10.1016\/j.bspc.2020.102027_bib0085","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"10.1016\/j.bspc.2020.102027_bib0090","article-title":"Selective kernel networks","author":"Li","year":"2019","journal-title":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.1016\/j.bspc.2020.102027_bib0095","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-Wr\u00f3blewska","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2020.102027_bib0100","doi-asserted-by":"crossref","first-page":"5561","DOI":"10.1118\/1.4962928","article-title":"Classification of breast lesions using segmented quantitative ultrasound maps of homodyned k distribution parameters","volume":"43","author":"Byra","year":"2016","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2020.102027_bib0105","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3390\/diagnostics9040182","article-title":"Classification of benign and malignant breast tumors using h-scan ultrasound imaging","volume":"9","author":"Ouyang","year":"2019","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2020.102027_bib0110","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 Brief"},{"key":"10.1016\/j.bspc.2020.102027_bib0115","article-title":"Deep learning approaches for data augmentation and classification of breast masses using ultrasound images","volume":"10","author":"Al-Dhabyani","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.bspc.2020.102027_bib0120","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/3DV.2016.79","article-title":"V-net. Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016","journal-title":"2016 Fourth International Conference on 3D Vision (3DV)"},{"key":"10.1016\/j.bspc.2020.102027_bib0125","first-page":"240","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"},{"key":"10.1016\/j.bspc.2020.102027_bib0130","series-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.bspc.2020.102027_bib0135","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016","journal-title":"OSDI, vol. 16"},{"key":"10.1016\/j.bspc.2020.102027_bib0140","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Rem. Sens. Lett."},{"key":"10.1016\/j.bspc.2020.102027_bib0145","doi-asserted-by":"crossref","first-page":"59037","DOI":"10.1109\/ACCESS.2019.2914873","article-title":"Attention dense-u-net for automatic breast mass segmentation in digital mammogram","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2020.102027_bib0150","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1002\/mrm.27969","article-title":"Knee menisci segmentation and relaxometry of 3d ultrashort echo time cones MR imaging using attention u-net with transfer learning","volume":"83","author":"Byra","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.bspc.2020.102027_bib0155","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1038\/s41467-018-07619-7","article-title":"Why rankings of biomedical image analysis competitions should be interpreted with care","volume":"9","author":"Maier-Hein","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2020.102027_bib0160","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1002\/mrm.27229","article-title":"Deep convolutional neural network for segmentation of knee joint anatomy","volume":"80","author":"Zhou","year":"2018","journal-title":"Magn. Reson. Med."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942030183X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942030183X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T19:08:54Z","timestamp":1761678534000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S174680942030183X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":32,"alternative-id":["S174680942030183X"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2020.102027","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2020,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2020.102027","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102027"}}