{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T06:13:21Z","timestamp":1760249601437,"version":"3.37.3"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000098","name":"NIH Clinical Center","doi-asserted-by":"publisher","award":["1Z01 CL040004"],"award-info":[{"award-number":["1Z01 CL040004"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-022-02782-1","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T20:19:11Z","timestamp":1667593151000},"page":"313-318","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Universal lymph node detection in T2 MRI using neural networks"],"prefix":"10.1007","volume":"18","author":[{"given":"Tejas Sudharshan","family":"Mathai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungwon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas C.","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"issue":"2","key":"2782_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3322\/caac.21388","volume":"67","author":"MB Amin","year":"2017","unstructured":"Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP (2017) The eighth edition ajcc cancer staging manual: continuing to build a bridge from a population-based to a more \u201cpersonalized\u2019\u2019 approach to cancer staging. CA Cancer J Clin 67(2):93\u201399","journal-title":"CA Cancer J Clin"},{"key":"2782_CR2","first-page":"321","volume-title":"Imaging of lymph nodes - mri and ct","author":"M Taupitz","year":"2007","unstructured":"Taupitz M (2007) Imaging of lymph nodes - mri and ct. Springer, pp 321\u2013329"},{"key":"2782_CR3","doi-asserted-by":"publisher","first-page":"102780","DOI":"10.1016\/j.ebiom.2020.102780","volume":"56","author":"Z Xingyu","year":"2020","unstructured":"Xingyu Z, Peiyi X, Mengmeng W, Pickhardt Perry J, Wei X, Fei X, Rui Z, Yao X, Junming J (2020) Deep learning based fully automated detection and segmentation of lymph nodes on multiparametric mri for rectal cancer: A multicentre study. EBioMedicine 56:102780","journal-title":"EBioMedicine"},{"key":"2782_CR4","doi-asserted-by":"publisher","first-page":"e8052","DOI":"10.7717\/peerj.8052","volume":"7","author":"OA Debats","year":"2019","unstructured":"Debats OA, Litjens GJS, Huisman HJ (2019) Lymph node detection in mr lymphography: false positive reduction using multi-view convolutional neural networks. Peer J 7:e8052","journal-title":"Peer J"},{"key":"2782_CR5","first-page":"5135","volume":"78 17","author":"L Yun","year":"2018","unstructured":"Yun L, Qiyue Yu, Gao Y, Zhou Y, Liu G, Dong Q, Ma J, Ding L, Yao H, Zhang Z, Xiao G, An Q, Wang G, Xi J, Yuan W-T, Lian Y, Zhang D, Zhao C-G, Yao Q, Liu W, Zhou X, Liu S, Qingyao W, Wenjian X, Zhang J, Wang D, Sun Z, Gao Y, Zhang X, Jilin H, Zhang M, Wang G, Zheng X, Wang L, Zhao J, Yang S (2018) Identification of metastatic lymph nodes in mr imaging with faster region-based convolutional neural networks. Cancer Res 78 17:5135\u20135143","journal-title":"Cancer Res"},{"key":"2782_CR6","doi-asserted-by":"publisher","first-page":"102345","DOI":"10.1016\/j.media.2021.102345","volume":"77","author":"S Wang","year":"2022","unstructured":"Wang S, Zhu Y, Lee S, Elton DC, Shen TC, Tang Y, Peng Y, Lu Z, Summers RM (2022) Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images. Med Image Anal 77:102345","journal-title":"Med Image Anal"},{"key":"2782_CR7","volume-title":"Machine Learning in Medical Imaging","author":"TS Mathai","year":"2021","unstructured":"Mathai TS, Lee S, Elton DC, Shen TC, Peng Y, Zhiyong L, Summers RM (2021) Detection of lymph nodes in t2 mri using neural network ensembles. In: Lian C, Cao X, Rekik I, Xuanang X, Yan P (eds) Machine Learning in Medical Imaging. Springer International Publishing, Cham"},{"key":"2782_CR8","doi-asserted-by":"crossref","unstructured":"Mathai TS, Lee S, Elton DC, Shen TC, Peng Y, Lu Z, Summers RM (2021) Lymph node detection in t2 mri with transformers. arXiv","DOI":"10.1117\/12.2613273"},{"key":"2782_CR9","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F, S\u00fcnderhauf N (2021) Varifocalnet: An IoU-aware Dense Object Detector, in 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021 pp. 8510\u20138519","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"2782_CR10","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer vision - ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12346 Springer International Publishing, Cham","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2782_CR11","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong T, Sun F, Liu H, Jiang Y, Li L, Shi J (2020) Foveabox: beyound anchor-based object detection. IEEE Trans Image Process 29:7389\u20137398","journal-title":"IEEE Trans Image Process"},{"key":"2782_CR12","volume-title":"Advances in Neural Information Processing Systems","author":"R Shaoqing","year":"2015","unstructured":"Shaoqing R, Kaiming H, Ross G, Jian S (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (eds) Advances in Neural Information Processing Systems. Curran Associates Inc, Red Hook"},{"key":"2782_CR13","doi-asserted-by":"crossref","unstructured":"Tang Y-B, Yan K, Tang Y-X, Liu J, Xiao J, Summers RM (2019) Uldor: a universal lesion detector for ct scans with pseudo masks and hard negative example mining. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp 833\u2013836","DOI":"10.1109\/ISBI.2019.8759478"},{"key":"2782_CR14","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"2782_CR15","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 936\u2013944","DOI":"10.1109\/CVPR.2017.106"},{"issue":"2","key":"2782_CR16","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2782_CR17","doi-asserted-by":"crossref","unstructured":"Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS\u2014improving object detection with one line of code. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2017.593"},{"key":"2782_CR18","doi-asserted-by":"publisher","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"R Solovyev","year":"2021","unstructured":"Solovyev R, Wang W, Gabruseva T (2021) Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis Comput 107:104117","journal-title":"Image Vis Comput"},{"key":"2782_CR19","doi-asserted-by":"crossref","unstructured":"Peng Y, Lee S, Elton DC, Shen T, Tang Y-X, Chen Q, Wang S, Zhu Y, Summers R, Lu (2020) Automatic recognition of abdominal lymph nodes from clinical text. In: Proceedings of the 3rd clinical natural language processing workshop, pp 101\u2013110, Online, November 2020. Association for Computational Linguistics","DOI":"10.18653\/v1\/2020.clinicalnlp-1.12"},{"issue":"6","key":"2782_CR20","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4itk: improved n3 bias correction. IEEE Trans Med Imaging 29(6):1310\u20131320","journal-title":"IEEE Trans Med Imaging"},{"key":"2782_CR21","doi-asserted-by":"crossref","unstructured":"Kocio\u0142ek M, Strzelecki M, Obuchowicz R (2020) Does image normalization and intensity resolution impact texture classification? Comput Med Imaging Graph 81:101716","DOI":"10.1016\/j.compmedimag.2020.101716"},{"key":"2782_CR22","unstructured":"Chen K, Wang J, Pang J, Yuhang Cao Y, Xiong Xiaoxiao L, Sun S, Feng W, Liu Z, Jiarui X, Zhang Z, Cheng D, Zhu C, Cheng T, Zhao Q, Li B, Xin L, Zhu R, Yue W, Dai J, Wang J, Shi J, Ouyang W, Loy Chen C, Lin D (2019) Mmdetection: open mmlab detection toolbox and benchmark"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02782-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02782-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02782-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T14:35:27Z","timestamp":1675175727000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02782-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,4]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["2782"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02782-1","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2022,11,4]]},"assertion":[{"value":"19 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"RMS receives royalties from iCAD, Philips, PingAn, ScanMed, and Translation Holdings. His lab received research support from PingAn. The authors have no additional conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this study, informed consent was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}