{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:17:14Z","timestamp":1791566234177,"version":"4.3.4"},"reference-count":215,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"School of Graduate Studies, UMKC, USA"},{"DOI":"10.13039\/501100004681","name":"Higher Education Commission, Pakistan, under US-Pakistan Knowledge Corridor Research Grant","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004681","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3086530","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T15:59:29Z","timestamp":1622822369000},"page":"83002-83024","source":"Crossref","is-referenced-by-count":102,"title":["Deep Neural Architectures for Medical Image Semantic Segmentation: Review"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2625-4936","authenticated-orcid":false,"given":"Muhammad Zubair","family":"Khan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4757-7723","authenticated-orcid":false,"given":"Mohan Kumar","family":"Gajendran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yugyung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muazzam A.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref170","first-page":"319","article-title":"Dilated convolutions in neural networks for left atrial segmentation in 3D gadolinium enhanced-MRI","author":"vesal","year":"2018","journal-title":"Proc Int Workshop Stat Atlases Comput Models Heart"},{"key":"ref172","first-page":"385","article-title":"Automatic brain structures segmentation using deep residual dilated U-Net","author":"li","year":"2018","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2870182"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.11.004"},{"key":"ref173","first-page":"287","article-title":"Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge","author":"isensee","year":"2017","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref176","article-title":"Recurrent models of visual attention","author":"mnih","year":"2014","journal-title":"arXiv 1406 6247"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.039"},{"key":"ref178","article-title":"Teaching machines to read and comprehend","author":"hermann","year":"2015","journal-title":"arXiv 1506 03340"},{"key":"ref177","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv 1409 0473"},{"key":"ref168","first-page":"309","article-title":"Brain tumor segmentation using dense fully convolutional neural network","author":"shaikh","year":"2017","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref169","first-page":"319","article-title":"ISLES challenge: U-shaped convolution neural network with dilated convolution for 3D stroke lesion segmentation","author":"tureckova","year":"2018","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00227-x"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"ref32","article-title":"MACU-Net for semantic segmentation of fine-resolution remotely sensed images","author":"li","year":"2020","journal-title":"arXiv 2007 13083"},{"key":"ref31","first-page":"171","article-title":"Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection","author":"jaeger","year":"2020","journal-title":"Proc Mach Learn Health Workshop"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7305-1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-017-9189-6"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-017-0141-z"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2020.0088"},{"key":"ref181","article-title":"Recent advances in recurrent neural networks","author":"salehinejad","year":"2017","journal-title":"arXiv 1801 01078"},{"key":"ref180","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"arXiv 1706 03762"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.07.005"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1613\/jair.614"},{"key":"ref183","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"oktay","year":"2018","journal-title":"arXiv 1804 03999"},{"key":"ref182","article-title":"Connection sensitive attention U-Net for accurate retinal vessel segmentation","author":"li","year":"1903","journal-title":"arXiv 1903 05558"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803101"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2020.00025"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"ref186","first-page":"450","article-title":"Ensembles of multiple models and architectures for robust brain tumour segmentation","author":"kamnitsas","year":"2017","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_79"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540016"},{"key":"ref179","article-title":"Attention-based models for speech recognition","author":"chorowski","year":"2015","journal-title":"arXiv 1506 07503"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1240"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899635"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_5"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1088\/978-0-7503-2216-4"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2019.05.041"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13764"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab843e"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3015108"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2613019"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref150","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8597606"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.03.001"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037243"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref56","first-page":"1","article-title":"COVINet: A convolutional neural network approach for predicting COVID-19 from chest X-ray images","author":"umer","year":"2021","journal-title":"J Ambient Intell Humanized Comput"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-823014-5.00006-5"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09854-1"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.02.001"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2927590"},{"key":"ref167","article-title":"MDU-Net: Multi-scale densely connected U-Net for biomedical image segmentation","author":"zhang","year":"2018","journal-title":"arXiv 1812 00352"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.03.049"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.12.022"},{"key":"ref163","article-title":"SuNet: A deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI","author":"cl\u00e8rigues","year":"2018","journal-title":"arXiv 1810 13304"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.08.025"},{"key":"ref161","first-page":"1","article-title":"Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI","volume":"32","author":"liu","year":"2019","journal-title":"Neural Comput Appl"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref4","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2791488"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.014"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2690863"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101998"},{"key":"ref159","article-title":"Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation","author":"alom","year":"2018","journal-title":"arXiv 1802 06955"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-02092-z"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2835303"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.11.005"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2013851"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3062380"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09854-1"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00140"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.323"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1051\/epjconf\/201921402010"},{"key":"ref70","article-title":"A survey on deep learning of small sample in biomedical image analysis","author":"zhang","year":"2019","journal-title":"arXiv 1908 00473"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_32"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-59497-3_175"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.118"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_60"},{"key":"ref60","first-page":"194","article-title":"LSTM neural networks for language modeling","author":"sundermeyer","year":"2012","journal-title":"Proc 13th Annu Conf Int Speech Commun Assoc"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref61","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref63","article-title":"A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises","author":"zhou","year":"2020","journal-title":"arXiv 2008 09104"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929365"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101813"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101985"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.mri.2019.05.043","article-title":"A review on brain tumor segmentation of MRI images","volume":"61","author":"anjali","year":"2019","journal-title":"Magn Reson Imag"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2019.2900628"},{"key":"ref69","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref197","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"chen","year":"2018","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_28"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"ref194","article-title":"SA-UNet: Spatial attention U-Net for retinal vessel segmentation","author":"guo","year":"2018","journal-title":"arXiv 1810 07810"},{"key":"ref195","article-title":"Study group learning: Improving retinal vessel segmentation trained with noisy labels","author":"zhou","year":"2021","journal-title":"arXiv 2103 03451"},{"key":"ref196","article-title":"RV-GAN: Segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network","author":"kamran","year":"2021","journal-title":"arXiv 2101 00535"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3001513"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00057"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.1.014006"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013714"},{"key":"ref191","article-title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","author":"zhuang","year":"2018","journal-title":"arXiv 1810 07810"},{"key":"ref92","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2014","journal-title":"arXiv 1412 6806"},{"key":"ref192","first-page":"3656","article-title":"IterNet: Retinal image segmentation utilizing structural redundancy in vessel networks","author":"li","year":"2020","journal-title":"Proc IEEE Winter Conf Appl Comput Vis (WACV)"},{"key":"ref91","first-page":"2449","article-title":"Spectral representations for convolutional neural networks","author":"rippel","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00874"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_65"},{"key":"ref96","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098328"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_94"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832217"},{"key":"ref84","first-page":"1","article-title":"Rethinking pre-training and self-training","volume":"33","author":"zoph","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref83","article-title":"Transfusion: Understanding transfer learning for medical imaging","author":"raghu","year":"2019","journal-title":"arXiv 1902 07208"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_90"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_42"},{"key":"ref86","article-title":"Generalizing to unseen domains via adversarial data augmentation","author":"volpi","year":"2018","journal-title":"arXiv 1805 12018"},{"key":"ref87","article-title":"Swish: A self-gated activation function","author":"ramachandran","year":"2017","journal-title":"arXiv 1710 05941"},{"key":"ref88","first-page":"1","article-title":"Stochastic pooling for regularization of deep convolutional neural networks: 1st international conference on learning representations","author":"zeiler","year":"2013","journal-title":"Proc 1st Int Conf Learn Represent (ICLR)"},{"key":"ref200","article-title":"Multi-level context gating of embedded collective knowledge for medical image segmentation","author":"asadi-aghbolaghi","year":"2020","journal-title":"arXiv 2003 05056"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1007\/s11936-020-00814-0"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.04.009"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00052"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.11.003"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.09.001"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.06.009"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.03.006"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2018.01.019"},{"key":"ref205","article-title":"Pancreas segmentation in CT and MRI images via domain specific network designing and recurrent neural contextual learning","author":"cai","year":"2018","journal-title":"arXiv 1803 11303"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.04.015"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.11.008"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12273"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-016-1582-x"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1715-9"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12746"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.05.006"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101638"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_16"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abc363"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.11.010"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_75"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_17"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3005297"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101556"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00864"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/42.845178"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1186\/s12886-018-0954-4"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.04.025"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_14"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2020.06.015"},{"key":"ref139","first-page":"178","article-title":"Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks","author":"wang","year":"2017","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref141","article-title":"Automatic skin lesion segmentation with fully convolutional-deconvolutional networks","author":"yuan","year":"2017","journal-title":"arXiv 1703 05165"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/ICB.2016.7550055"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2783305"},{"key":"ref144","first-page":"424","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2891970"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2959609"},{"key":"ref109","first-page":"1","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc NIPS"},{"key":"ref108","author":"gulli","year":"2017","journal-title":"Deep LearningWith Keras"},{"key":"ref107","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv 1603 04467"},{"key":"ref106","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","author":"wan","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref105","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012","journal-title":"arXiv 1207 0580"},{"key":"ref104","first-page":"318","article-title":"Anatomical data augmentation via fluid-based image registration","author":"shen","year":"2020","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_38"},{"key":"ref102","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","author":"verma","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_3"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.12.002"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2759102"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2325029"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2017.2715350"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2020.00025"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11684-020-0782-9"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2961792"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.232"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_47"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3005317"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00918"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.04.002"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908685"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3001502"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.10.012"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_59"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13349"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2895894"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2743464"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00968"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_65"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.03.014"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09447006.pdf?arnumber=9447006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T14:56:27Z","timestamp":1639752987000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9447006\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":215,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3086530","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}