{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:22:11Z","timestamp":1740169331378,"version":"3.37.3"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Ministry of Health & Welfare, Republic of Korea","doi-asserted-by":"publisher","award":["HI21C1161"],"award-info":[{"award-number":["HI21C1161"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"DOI":"10.13039\/501100014188","name":"the Ministry of Science and ICT (MSIT), Republic of Korea","doi-asserted-by":"publisher","award":["2020-0-01336"],"award-info":[{"award-number":["2020-0-01336"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Artificial Intelligence Graduate School Program"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Ministry of Science and ICT (MSIT), Republic of Korea","doi-asserted-by":"publisher","award":["NRF-2021R1F1A1057818","NRF-2022R1A2C201119112"],"award-info":[{"award-number":["NRF-2021R1F1A1057818","NRF-2022R1A2C201119112"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3281558","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T18:12:34Z","timestamp":1685556754000},"page":"55117-55125","source":"Crossref","is-referenced-by-count":1,"title":["TS-Net: A Deep Learning Framework for Automated Assessment of Longitudinal Tumor Volume Changes in an Orthotopic Breast Cancer Model Using MRI"],"prefix":"10.1109","volume":"11","author":[{"given":"Yunkyoung","family":"Jun","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9191-8678","authenticated-orcid":false,"given":"Seokha","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7959-3395","authenticated-orcid":false,"given":"Noehyun","family":"Myung","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiwoo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5380-7674","authenticated-orcid":false,"given":"Hyung Joon","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref12","first-page":"261","article-title":"Diagnostic accuracy of MRI in predicting breast tumor size: Comparative analysis of MRI vs histopathological assessed breast tumor size","volume":"79","author":"jethava","year":"2015","journal-title":"Conn Med"},{"key":"ref34","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref15","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":"ref37","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","article-title":"Review of MRI-based brain tumor image segmentation using deep learning methods","volume":"102","author":"i??n","year":"2016","journal-title":"Proc Journal of Computer Science"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/info11020125","article-title":"Albumentations: Fast and flexible image augmentations","volume":"11","author":"buslaev","year":"2020","journal-title":"Information"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-021-01413-y"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899635"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MC.1983.1654163"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004056"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12207"},{"key":"ref17","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","author":"zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 4th International Workshop DLMIA 2018 and 8th International Workshop ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada Spain September 20 2018 Proceedings 4"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.20982\/tqmp.04.1.p013"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929270"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2021.06.017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3791\/58604"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11095-020-2758-5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/BF00300234"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23829"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214587"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2931922"},{"key":"ref28","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":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jconrel.2022.10.054"},{"key":"ref29","article-title":"Deep learning using rectified linear units (ReLU)","author":"agarap","year":"2018","journal-title":"arXiv 1803 08375"},{"key":"ref8","first-page":"194","article-title":"Advances in imaging mouse tumour models in vivo","volume":"205","author":"lyons","year":"2005","journal-title":"The Journal of Pathology A Journal of the Pathological Society of Great Britain and Ireland"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0b013e32830719d4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8140(97)00215-6"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1476-5381.2012.01916.x"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.4103\/0975-7406.124301"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.7863\/jum.2010.29.6.891"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1023\/A:1006140513233"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10138909.pdf?arnumber=10138909","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T18:27:23Z","timestamp":1688408843000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10138909\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3281558","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}