{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T06:03:25Z","timestamp":1773295405757,"version":"3.50.1"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013910","name":"University of the Chinese Academy of Sciences Wenzhou Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013910","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.compbiomed.2024.108393","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T15:19:21Z","timestamp":1712071161000},"page":"108393","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes"],"prefix":"10.1016","volume":"174","author":[{"given":"Xinlong","family":"Xing","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7364-630X","authenticated-orcid":false,"given":"Xiaosen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chaoyi","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Zhantian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ou","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Senmiao","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Haoman","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shichao","family":"Quan","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jianwei","family":"Shuai","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2024.108393_bib1","series-title":"Radiation Dose in X-Ray and CT Exams","author":"Safety","year":"2012"},{"key":"10.1016\/j.compbiomed.2024.108393_bib2","article-title":"Pyramid channel-based feature attention network for image dehazing","volume":"197","author":"Zhang","year":"2020"},{"key":"10.1016\/j.compbiomed.2024.108393_bib3","first-page":"1141","article-title":"Bioinspired polarized skylight orientation determination artificial neural network","volume":"20","author":"Liang","year":"2023","journal-title":"JBE"},{"key":"10.1016\/j.compbiomed.2024.108393_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107596","article-title":"SUnet: a multi-organ segmentation network based on multiple attention","volume":"167","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105760","article-title":"Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement","volume":"147","author":"Hu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107326","article-title":"BiFTransNet: a unified and simultaneous segmentation network for gastrointestinal images of CT & MRI","volume":"165","author":"Jiang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105810","article-title":"Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation","volume":"148","author":"Qi","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105618","article-title":"Multilevel threshold image segmentation for COVID-19 chest radiography: a framework using horizontal and vertical multiverse optimization","volume":"146","author":"Su","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2019.107139","article-title":"Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation","volume":"83","author":"Liu","year":"2019","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.compbiomed.2024.108393_bib10","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202300224","article-title":"Revolutionizing infection risk scoring: an ensemble \u201cfrom weak to strong\u201d deduction strategy and enhanced point\u2010of\u2010care testing tools","volume":"5","author":"Chen","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.compbiomed.2024.108393_bib11","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad259","article-title":"Predicting metabolite\u2013disease associations based on auto-encoder and non-negative matrix factorization","volume":"24","author":"Gao","year":"2023","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.compbiomed.2024.108393_bib12","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2022.1090770","article-title":"Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients","volume":"13","author":"Wei","year":"2023","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.compbiomed.2024.108393_bib13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13755-023-00268-1","article-title":"MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis","volume":"12","author":"Zhu","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.compbiomed.2024.108393_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106733","article-title":"Gene function and cell surface protein association analysis based on single-cell multiomics data","volume":"157","author":"Hu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib15","doi-asserted-by":"crossref","first-page":"bbac463","DOI":"10.1093\/bib\/bbac463","article-title":"Predicting the potential human lncRNA\u2013miRNA interactions based on graph convolution network with conditional random field","volume":"23","author":"Wang","year":"2022","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.compbiomed.2024.108393_bib16","doi-asserted-by":"crossref","first-page":"bbac527","DOI":"10.1093\/bib\/bbac527","article-title":"Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods","volume":"24","author":"Zhao","year":"2023","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.compbiomed.2024.108393_bib17","first-page":"179","article-title":"Dear-DIAXMBD: deep autoencoder enables deconvolution of data-independent acquisition","volume":"6","author":"He","year":"2023","journal-title":"Proteomics, Research"},{"key":"10.1016\/j.compbiomed.2024.108393_bib18","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/s41556-022-00854-7","article-title":"Mosaic composition of RIP1\u2013RIP3 signalling hub and its role in regulating cell death","volume":"24","author":"Chen","year":"2022","journal-title":"Nat. Cell Biol."},{"key":"10.1016\/j.compbiomed.2024.108393_bib19","series-title":"A deep learning architecture for limited-angle computed tomography reconstruction, Bildverarbeitung f\u00fcr die Medizin 2017","first-page":"92","author":"Hammernik","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib20","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1111\/cgf.13369","article-title":"Single\u2010image tomography: 3D volumes from 2D cranial x\u2010rays","volume":"37","author":"Henzler","year":"2018","journal-title":"Comput. Graph. Forum"},{"key":"10.1016\/j.compbiomed.2024.108393_bib21","series-title":"Machine Learning for Medical Image Reconstruction","first-page":"123","article-title":"End-to-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images","author":"Kasten","year":"2020"},{"key":"10.1016\/j.compbiomed.2024.108393_bib22","series-title":"International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)","first-page":"400","article-title":"X2teeth: 3d teeth reconstruction from a single panoramic radiograph","author":"Liang","year":"2020"},{"key":"10.1016\/j.compbiomed.2024.108393_bib23","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1038\/s41551-019-0466-4","article-title":"Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning","volume":"3","author":"Shen","year":"2019","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2024.108393_bib24","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"432","article-title":"Deep learning computed tomography","author":"W\u00fcrfl","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108393_bib25","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"10619","article-title":"X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks","author":"Ying","year":"2019"},{"key":"10.1016\/j.compbiomed.2024.108393_bib26","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/5.5962","article-title":"Ill-posed problems in early vision","volume":"76","author":"Bertero","year":"1988","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.compbiomed.2024.108393_bib27","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.2024.108393_bib28","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108393_bib29","series-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.compbiomed.2024.108393_bib30","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108393_bib31","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib32","series-title":"arXiv Preprint arXiv:1704.04861","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib33","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"6848","article-title":"Shufflenet: an extremely efficient convolutional neural network for mobile devices","author":"Zhang","year":"2018"},{"key":"10.1016\/j.compbiomed.2024.108393_bib34","series-title":"European Conference on Computer Vision (ECCV)","first-page":"628","article-title":"3d-r2n2: a unified approach for single and multi-view 3d object reconstruction","author":"Choy","year":"2016"},{"key":"10.1016\/j.compbiomed.2024.108393_bib35","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1145\/1276377.1276487","article-title":"Approximate image-based tree-modeling using particle flows","volume":"26","author":"Neubert","year":"2007","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.compbiomed.2024.108393_bib36","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2897","article-title":"Multi-view consistency as supervisory signal for learning shape and pose prediction","author":"Tulsiani","year":"2018"},{"key":"10.1016\/j.compbiomed.2024.108393_bib37","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"605","article-title":"A point set generation network for 3d object reconstruction from a single image","author":"Fan","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib38","first-page":"1","article-title":"Automatic reconstruction of tree skeletal structures from point clouds","author":"Livny","year":"2010","journal-title":"Proc. of ACM SIGGRAPH Asia"},{"key":"10.1016\/j.compbiomed.2024.108393_bib39","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1080\/21681163.2014.913990","article-title":"3D reconstruction of rib cage geometry from biplanar radiographs using a statistical parametric model approach","volume":"4","author":"Aubert","year":"2016","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"10.1016\/j.compbiomed.2024.108393_bib40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11548-009-0390-2","article-title":"3D reconstruction of the human rib cage from 2D projection images using a statistical shape model","volume":"5","author":"Dworzak","year":"2010","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.compbiomed.2024.108393_bib41","series-title":"Proceedings of the International Conference on Pattern Recognition (ICPR)","first-page":"371","article-title":"Atlas-based 3D-shape reconstruction from X-ray images","author":"Lamecker","year":"2006"},{"key":"10.1016\/j.compbiomed.2024.108393_bib42","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MCG.2010.12","article-title":"Knowledge-assisted reconstruction of the human rib cage and lungs","volume":"30","author":"Koehler","year":"2010","journal-title":"IEEE Comput Graph Appl"},{"key":"10.1016\/j.compbiomed.2024.108393_bib43","series-title":"International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)","first-page":"259","article-title":"Image-to-graph convolutional network for deformable shape reconstruction from a single projection image","author":"Nakao","year":"2021"},{"key":"10.1016\/j.compbiomed.2024.108393_bib44","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"10.1016\/j.compbiomed.2024.108393_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102067","article-title":"XctNet: reconstruction network of volumetric images from a single X-ray image","volume":"98","author":"Tan","year":"2022","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.compbiomed.2024.108393_bib46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.compbiomed.2024.108393_bib47","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4681","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","author":"Ledig","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib48","series-title":"arXiv Preprint arXiv:1712.05927","article-title":"SRPGAN: perceptual generative adversarial network for single image super resolution","author":"Wu","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib49","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2223","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107024","article-title":"MS-ACGAN: a modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data","volume":"162","author":"Jahanyar","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib51","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1002\/ima.22428","article-title":"High\u2010quality retinal vessel segmentation using generative adversarial network with a large receptive field","volume":"30","author":"Zhao","year":"2020","journal-title":"Int. J. Imag. Syst. Technol."},{"key":"10.1016\/j.compbiomed.2024.108393_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106641","article-title":"Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals","volume":"155","author":"Wang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2024.108393_bib53","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"3865","article-title":"Generating synthetic computed tomography (CT) images to improve the performance of machine learning model for pediatric abdominal anomaly detection","author":"Bhattacharya","year":"2023"},{"key":"10.1016\/j.compbiomed.2024.108393_bib54","first-page":"566","article-title":"Oral-3d: reconstructing the 3d structure of oral cavity from panoramic x-ray","author":"Song","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.compbiomed.2024.108393_bib55","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.3390\/diagnostics12051121","article-title":"Generative adversarial network (GAN) for automatic reconstruction of the 3D spine structure by using simulated Bi-planar X-ray images","volume":"12","author":"Yang","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.compbiomed.2024.108393_bib56","series-title":"Rigid-motion Scattering for Texture Classification","author":"Sifre","year":"2014"},{"key":"10.1016\/j.compbiomed.2024.108393_bib57","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1233","article-title":"Rotation, scaling and deformation invariant scattering for texture discrimination","author":"Sifre","year":"2013"},{"key":"10.1016\/j.compbiomed.2024.108393_bib58","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.compbiomed.2024.108393_bib59","series-title":"International Conference on Machine Learning","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.compbiomed.2024.108393_bib60","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib61","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2794","article-title":"Least squares generative adversarial networks","author":"Mao","year":"2017"},{"key":"10.1016\/j.compbiomed.2024.108393_bib62","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"802","article-title":"Gal: geometric adversarial loss for single-view 3d-object reconstruction","author":"Jiang","year":"2018"},{"key":"10.1016\/j.compbiomed.2024.108393_bib63","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans","volume":"38","author":"Armato","year":"2011","journal-title":"Med. Phys."},{"key":"10.1016\/j.compbiomed.2024.108393_bib64","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)? \u2013 Arguments against avoiding RMSE in the literature, Geosci","volume":"7","author":"Chai","year":"2014","journal-title":"Model Develop"},{"key":"10.1016\/j.compbiomed.2024.108393_bib65","series-title":"2010 20th International Conference on Pattern Recognition","first-page":"2366","article-title":"Image quality metrics: PSNR vs. SSIM","author":"Hore","year":"2010"},{"key":"10.1016\/j.compbiomed.2024.108393_bib66","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compbiomed.2024.108393_bib67","series-title":"The 7th International Student Conference on Advanced Science and Technology (ICAST)","first-page":"1","article-title":"Semantic cosine similarity","author":"Rahutomo","year":"2012"},{"key":"10.1016\/j.compbiomed.2024.108393_bib68","first-page":"1","article-title":"An effective method for the protection of user health topic privacy for health information services","author":"Wu","year":"2023","journal-title":"World Wide Web"},{"key":"10.1016\/j.compbiomed.2024.108393_bib69","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2024.102343","article-title":"Secure multi-dimensional data retrieval with access control and range query in the cloud","volume":"122","author":"Mei","year":"2024","journal-title":"Inf. Syst."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524004773?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482524004773?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T01:34:17Z","timestamp":1738373657000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482524004773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":69,"alternative-id":["S0010482524004773"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108393","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108393","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"108393"}}