Zobrazeno 1 - 10
of 1 636
pro vyhledávání: '"Dinggang Shen"'
Autor:
Kaicong Sun, Yuanwang Zhang, Jiameng Liu, Ling Yu, Yan Zhou, Fang Xie, Qihao Guo, Han Zhang, Qian Wang, Dinggang Shen
Publikováno v:
Communications Engineering, Vol 3, Iss 1, Pp 1-13 (2024)
Abstract Brain disease diagnosis using multiple imaging modalities has shown superior performance compared to using single modality, yet multi-modal data is not easily available in clinical routine due to cost or radiation risk. Here we propose a syn
Externí odkaz:
https://doaj.org/article/7d5bfb0002cd4e1abb53199ed2cf900b
Autor:
Lin Yan, Zhiying Liang, Hao Zhang, Gaosong Zhang, Weiwei Zheng, Chunguang Han, Dongsheng Yu, Hanqi Zhang, Xinxin Xie, Chang Liu, Wenxin Zhang, Hui Zheng, Jing Pei, Dinggang Shen, Xuejun Qian
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-11 (2024)
Abstract Background Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deploy
Externí odkaz:
https://doaj.org/article/65eb8cb1c48f4cd08ce659cefda91cd8
Autor:
Qingxia Wu, Huali Li, Yan Wang, Yan Bai, Yaping Wu, Xuan Yu, Xiaodong Li, Pei Dong, Jon Xue, Dinggang Shen, Meiyun Wang
Publikováno v:
JMIR Medical Informatics, Vol 12, p e55799 (2024)
BackgroundLarge language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. ObjectiveThis study aims to evaluate
Externí odkaz:
https://doaj.org/article/e089eb15dcd54c80ab77106bddfb166f
Autor:
Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Chao Cao, Haixing Dai, Ninghao Liu, Jun Liu, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Publikováno v:
Frontiers in Radiology, Vol 3 (2024)
At the dawn of of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However
Externí odkaz:
https://doaj.org/article/4aee005b9143410bb62b7c8b88ede6dc
Autor:
Jiadong Zhang, Kaicong Sun, Junwei Yang, Yan Hu, Yuning Gu, Zhiming Cui, Xiaopeng Zong, Fei Gao, Dinggang Shen
Publikováno v:
Communications Engineering, Vol 2, Iss 1, Pp 1-13 (2023)
Abstract Medical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is diff
Externí odkaz:
https://doaj.org/article/da07dc03b3c246f1978dfbf4cc0107f6
Autor:
Lei Jin, Tianyang Sun, Xi Liu, Zehong Cao, Yan Liu, Hong Chen, Yixin Ma, Jun Zhang, Yaping Zou, Yingchao Liu, Feng Shi, Dinggang Shen, Jinsong Wu
Publikováno v:
iScience, Vol 26, Iss 11, Pp 108041- (2023)
Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected
Externí odkaz:
https://doaj.org/article/e6076dff1d32436684143425585f0b30
Autor:
Shuhua Ren, Yongsheng Pan, Junpeng Li, Lin Huang, Liang Cui, Donglang Jiang, Qi Huang, Yihui Guan, Qihao Guo, Dinggang Shen, Fang Xie
Publikováno v:
View, Vol 4, Iss 5, Pp n/a-n/a (2023)
Abstract Visual interpretation is considered the gold standard for amyloid scans in clinical practice. However, dichotomous classification of amyloid deposition by visual reading always results in bias due to rater experience. Therefore, there is a n
Externí odkaz:
https://doaj.org/article/3fc3363f8fe6406983aa15ad5079fea6
Autor:
Weixiong Jiang, Zhen Zhou, Guoshi Li, Weiyan Yin, Zhengwang Wu, Li Wang, Maryam Ghanbari, Gang Li, Pew-Thian Yap, Brittany R. Howell, Martin A. Styner, Essa Yacoub, Heather Hazlett, John H. Gilmore, J. Keith Smith, Kamil Ugurbil, Jed T. Elison, Han Zhang, Dinggang Shen, Weili Lin
Publikováno v:
Developmental Cognitive Neuroscience, Vol 63, Iss , Pp 101284- (2023)
Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regi
Externí odkaz:
https://doaj.org/article/04cfaf29e7a94f2b8ec2f8eea06f4b60
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
Autor:
Feng Shi, Weigang Hu, Jiaojiao Wu, Miaofei Han, Jiazhou Wang, Wei Zhang, Qing Zhou, Jingjie Zhou, Ying Wei, Ying Shao, Yanbo Chen, Yue Yu, Xiaohuan Cao, Yiqiang Zhan, Xiang Sean Zhou, Yaozong Gao, Dinggang Shen
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Volume delineation of organs-at risk (OARs) and target tumors is an indispensable process for creating radiotherapy treatment planning. Herein, the authors propose a lightweight deep learning framework to empower the rapid and precise volume delineat
Externí odkaz:
https://doaj.org/article/7b539ad070e747a0a435de4ce3f66f02
Autor:
Jiaojiao Wu, Yuwei Xia, Xuechun Wang, Ying Wei, Aie Liu, Arun Innanje, Meng Zheng, Lei Chen, Jing Shi, Liye Wang, Yiqiang Zhan, Xiang Sean Zhou, Zhong Xue, Feng Shi, Dinggang Shen
Publikováno v:
Frontiers in Radiology, Vol 3 (2023)
IntroductionMedical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on
Externí odkaz:
https://doaj.org/article/81b4b1fe198d4000915af53cc0fb2886