Zobrazeno 1 - 10
of 10 427
pro vyhledávání: '"MULTI-PARAMETRIC MRI"'
Autor:
Rui, Shaohao, Chen, Lingzhi, Tang, Zhenyu, Wang, Lilong, Liu, Mianxin, Zhang, Shaoting, Wang, Xiaosong
Accurate diagnosis of brain abnormalities is greatly enhanced by the inclusion of complementary multi-parametric MRI imaging data. There is significant potential to develop a universal pre-training model that can be quickly adapted for image modaliti
Externí odkaz:
http://arxiv.org/abs/2410.10604
Autor:
Eidex, Zach, Safari, Mojtaba, Qiu, Richard L. J., Yu, David S., Shu, Hui-Kuo, Mao, Hui, Yang, Xiaofeng
Objective: Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develop
Externí odkaz:
http://arxiv.org/abs/2409.01622
Autor:
Lihua Chen, Yan Ren, Yizhong Yuan, Jipan Xu, Baole Wen, Shuangshuang Xie, Jinxia Zhu, Wenshuo Li, Xiaoli Gong, Wen Shen
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-in
Externí odkaz:
https://doaj.org/article/4a0c68bdff3646cc878909c4fa863233
Autor:
Allam, Ibrahim M.1 hem.urology@gmail.com, Salem, Tarek A.1, Ali, Mohamed H.1, Zaza, Mohammed M.1
Publikováno v:
Egyptian Journal of Hospital Medicine. Jul2024, Vol. 96, p2791-2795. 5p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for training is laborious and time-consuming. Therefore, it is crucial to develop effective algor
Externí odkaz:
http://arxiv.org/abs/2401.01654
Autor:
Chatterjee, Aritrick1,2 (AUTHOR) aritrick@uchicago.edu, Fan, Xiaobing1 (AUTHOR) ayousuf@uchicagomedicine.org, Slear, Jessica1 (AUTHOR) mmedved@uchicago.edu, Asare, Gregory1 (AUTHOR) gskarczm@uchicago.edu, Yousuf, Ambereen N.1,2 (AUTHOR) aoto@bsd.uchicago.edu, Medved, Milica1,2 (AUTHOR), Antic, Tatjana3 (AUTHOR) tatjana.antic@bsd.uchicago.edu, Eggener, Scott4 (AUTHOR) seggener@bsd.uchicago.edu, Karczmar, Gregory S.1,2 (AUTHOR), Oto, Aytekin1,2 (AUTHOR)
Publikováno v:
Cancers. Oct2024, Vol. 16 Issue 20, p3499. 13p.
Autor:
Chen, Jingyun, Yuan, Yading
Federated Learning (FL) enables collaborative model training among medical centers without sharing private data. However, traditional FL risks on server failures and suboptimal performance on local data due to the nature of centralized model aggregat
Externí odkaz:
http://arxiv.org/abs/2401.15434
Autor:
Gumus, Kazim Z.1 (AUTHOR) kazim.gumus@jax.ufl.edu, Nicolas, Julien2 (AUTHOR), Gopireddy, Dheeraj R.1 (AUTHOR), Dolz, Jose2 (AUTHOR), Jazayeri, Seyed Behzad3 (AUTHOR) mark.bandyk@jax.ufl.edu, Bandyk, Mark3 (AUTHOR)
Publikováno v:
Cancers. Jul2024, Vol. 16 Issue 13, p2348. 10p.