Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Pingkun Yan"'
Publikováno v:
BioMedInformatics, Vol 4, Iss 2, Pp 889-910 (2024)
Background: Machine learning (ML) and artificial intelligence (AI)-based classifiers can be used to diagnose diseases from medical imaging data. However, few of the classifiers proposed in the literature translate to clinical use because of robustnes
Externí odkaz:
https://doaj.org/article/363866ae710b43e1939be39e4bcad624
Publikováno v:
Visual Computing for Industry, Biomedicine, and Art, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons
Externí odkaz:
https://doaj.org/article/73194d0ff1814f3097ec292ab80c71b9
Autor:
Diego Machado Reyes, Hanqing Chao, Juergen Hahn, Li Shen, Pingkun Yan, for the Alzheimer’s Disease Neuroimaging Initiative
Publikováno v:
Journal of Personalized Medicine, Vol 14, Iss 4, p 421 (2024)
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the dise
Externí odkaz:
https://doaj.org/article/11a7709b46ce40ad84698cfe1e3a6400
Autor:
Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Here, the authors develop a deep learning model to perform this task, showing human-level performance.
Externí odkaz:
https://doaj.org/article/95de7da9fa1641daa7bb2a30bce80781
Autor:
Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shaoyu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
Publikováno v:
Patterns, Vol 3, Iss 5, Pp 100475- (2022)
Summary: Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is a special case of the Lipschitz continuit
Externí odkaz:
https://doaj.org/article/00be61a3a58543d095d8f90f4e171dc3
Autor:
Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shaoyu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
Publikováno v:
Patterns, Vol 3, Iss 5, Pp 100474- (2022)
Summary: A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply r
Externí odkaz:
https://doaj.org/article/d5949e76997e47be8bfc088ade2a16f5
Publikováno v:
Journal of Personalized Medicine, Vol 12, Iss 8, p 1314 (2022)
There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i
Externí odkaz:
https://doaj.org/article/5709f30e3b6e45c3a67d307814be3c3d
Autor:
Yuanyuan Gao, Lora Cavuoto, Anirban Dutta, Uwe Kruger, Pingkun Yan, Arun Nemani, Jack E. Norfleet, Basiel A. Makled, Jessica Silvestri, Steven Schwaitzberg, Xavier Intes, Suvranu De
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
Acquisition of fine motor skills is a time-consuming process as it is based on learning via frequent repetitions. Transcranial electrical stimulation (tES) is a promising means of enhancing simple motor skill development via neuromodulatory mechanism
Externí odkaz:
https://doaj.org/article/98efe3fc074e4feb8705ae720ff4f73c
Publikováno v:
Complexity, Vol 2018 (2018)
Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation
Externí odkaz:
https://doaj.org/article/76f3f6e387454eb9b30936143af905fa
Publikováno v:
International Journal of Biomedical Imaging, Vol 2012 (2012)
Externí odkaz:
https://doaj.org/article/35f372813d904f8d80f10d870edf2860