{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:55:12Z","timestamp":1775066112766,"version":"3.50.1"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Bourns Endowment Funds"},{"DOI":"10.13039\/100006098","name":"Radiological Society of North America","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tai.2024.3440219","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T14:55:18Z","timestamp":1723128918000},"page":"6442-6456","source":"Crossref","is-referenced-by-count":3,"title":["A Comprehensive Radiogenomic Feature Characterization of 19\/20 Co-gain in Glioblastoma"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0128-6691","authenticated-orcid":false,"given":"Padmaja","family":"Jonnalagedda","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7992-1747","authenticated-orcid":false,"given":"Brent","family":"Weinberg","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7105-7886","authenticated-orcid":false,"given":"Taejin L.","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0863-0505","authenticated-orcid":false,"given":"Shiv","family":"Bhanu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Riverside Community Hospital, Riverside, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8971-6416","authenticated-orcid":false,"given":"Bir","family":"Bhanu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14164052"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191832"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-022-01237-0"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/fi14120351"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621432"},{"key":"ref6","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"Brock","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106371"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11060-017-2379-y"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25870"},{"key":"ref11","first-page":"857","article-title":"Correlation via synthesis: end-to-end image generation and radiogenomic learning based on generative adversarial network","volume":"121","author":"Xu","year":"2020","journal-title":"Med. Imag. Deep Learn."},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101906","article-title":"Hlioblastoma multiforme prognosis: MRI missing modality generation, segmentation and radiogenomic survival prediction","volume":"91","author":"Islam","year":"2021","journal-title":"Comput. Med. Imag. Graph."},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2637828"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s13277-013-0934-5"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2009.197"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-89477-w"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2018.00033"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.a5667"},{"key":"ref19","first-page":"349","article-title":"Feature disentanglement to aid imaging biomarker characterization for genetic mutations","volume":"121","author":"Jonnalagedda","year":"2020","journal-title":"Med. Imag. Deep Learn."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412151"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3071466"},{"key":"ref22","article-title":"Decomposing motion and content for natural video sequence prediction","author":"Villegas","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2835143"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104254"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5114\/wo.2014.47136"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CONECCT50063.2020.9198672"},{"key":"ref28","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"Karras","year":"2017"},{"key":"ref29","first-page":"5767","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gulrajani","year":"2017"},{"key":"ref30","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Heusel","year":"2017"},{"key":"ref31","article-title":"A note on the inception score","author":"Barratt","year":"2018"},{"key":"ref32","article-title":"LR-GAN: Layered recursive generative adversarial networks for image generation","author":"Yang","year":"2017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3006925"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00820"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00444"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002355"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1093\/noajnl\/vdad009"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2637828"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110786"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102470"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103329"},{"key":"ref42","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho","year":"2020"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s10555-021-09997-9"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26907"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14131731"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3155788"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_14"},{"key":"ref49","article-title":"Wasserstein GAN","author":"Arjovsky","year":"2017"},{"key":"ref50","first-page":"1","article-title":"Artificial MRI image generation using deep convolutional GAN and its comparison with other augmentation methods","volume-title":"Proc. Int. Conf. Commun., Control Inf. Sci. (ICCISc)","author":"Rejusha","year":"2021"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363678"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.5220\/0007363900002108"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896409"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357890"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947606"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8950-4_27"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2024.3440219"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10794552\/10631666.pdf?arnumber=10631666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:26Z","timestamp":1755911366000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10631666\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":59,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3440219","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}