{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T15:09:28Z","timestamp":1771686568814,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R and D Program of China under Grand","award":["MOST-2018AAA0102004"],"award-info":[{"award-number":["MOST-2018AAA0102004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tip.2021.3112053","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T19:55:40Z","timestamp":1631908540000},"page":"7980-7994","source":"Crossref","is-referenced-by-count":17,"title":["Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7699-0894","authenticated-orcid":false,"given":"Churan","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-5927","authenticated-orcid":false,"given":"Jing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fandong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinwei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Dong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0470-5548","authenticated-orcid":false,"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9888-6409","authenticated-orcid":false,"given":"Yizhou","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1118\/1.3560462"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_63"},{"key":"ref33","first-page":"37","volume":"2014","author":"sickles","year":"2013","journal-title":"ACR BI-RADS Atlas Breast Imaging Reporting and Data System"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-22437-z"},{"key":"ref37","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","first-page":"618","article-title":"An automatic mass detection system in mammograms based on complex texture features","volume":"18","author":"tai","year":"2013","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00028"},{"key":"ref10","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00946-5_11"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"ref12","first-page":"11","article-title":"Multiparametric magnetic resonance image synthesis using generative adversarial networks","author":"haarburger","year":"2019","journal-title":"Proc VCBM"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","article-title":"In defense of the triplet loss for person re-identification","author":"hermans","year":"2017","journal-title":"arXiv 1703 07737"},{"key":"ref15","first-page":"6626","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","author":"heusel","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00256"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref19","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"ref4","article-title":"Counterfactuals uncover the modular structure of deep generative models","author":"besserve","year":"2020","journal-title":"Proc 8th Int Conf Learn Represent (ICLR)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/int.20080"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.3982\/ECTA10582","article-title":"Inference on counterfactual distributions","volume":"81","author":"chernozhukov","year":"2013","journal-title":"Econometrica"},{"key":"ref29","article-title":"CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning","author":"rajpurkar","year":"2017","journal-title":"arXiv 1711 05225"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1097\/00008469-199610000-00003"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_13"},{"key":"ref7","first-page":"4467","article-title":"Dual path networks","author":"chen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/48\/20\/011"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01096"},{"key":"ref1","first-page":"40","article-title":"Learning representations and generative models for 3D point clouds","author":"achlioptas","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_69"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0324-9"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_53"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019181113"},{"key":"ref42","article-title":"Pre-training on grayscale imagenet improves medical image classification","author":"xie","year":"2018","journal-title":"Proc Eur Conf Comput Vis (ECCV) Workshops"},{"key":"ref24","article-title":"Conditional WGANs with adaptive gradient balancing for sparse MRI reconstruction","author":"malkiel","year":"2019","journal-title":"arXiv 1905 00985"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2945514"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_20"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref26","first-page":"7860","article-title":"Breaking the cycle–colleagues are all you need","author":"nizan","year":"2020","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2785879"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2011.09.014"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9263394\/09540781.pdf?arnumber=9540781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:51:10Z","timestamp":1652194270000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9540781\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/tip.2021.3112053","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}