Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Wei, Qingyue"'
Autor:
Sivaraman, Vishal Balaji, Imran, Muhammad, Wei, Qingyue, Muralidharan, Preethika, Tamplin, Michelle R., Grumbach, Isabella M ., Kardon, Randy H., Wang, Jui-Kai, Zhou, Yuyin, Shao, Wei
We introduce the RetinaRegNet model, which can achieve state-of-the-art performance across various retinal image registration tasks. RetinaRegNet does not require training on any retinal images. It begins by establishing point correspondences between
Externí odkaz:
http://arxiv.org/abs/2404.16017
The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medical imaging presents challenges, requiring either substantial training
Externí odkaz:
http://arxiv.org/abs/2403.18271
Autor:
Chen, Jieneng, Mei, Jieru, Li, Xianhang, Lu, Yongyi, Yu, Qihang, Wei, Qingyue, Luo, Xiangde, Xie, Yutong, Adeli, Ehsan, Wang, Yan, Lungren, Matthew, Xing, Lei, Lu, Le, Yuille, Alan, Zhou, Yuyin
Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, has proven highly successful for various medical image segmentation tas
Externí odkaz:
http://arxiv.org/abs/2310.07781
Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this burden, semi-supervised learning has garnered attention as a potentia
Externí odkaz:
http://arxiv.org/abs/2307.11604
The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate
Externí odkaz:
http://arxiv.org/abs/2211.07363
Autor:
Yan, Rui, Qu, Liangqiong, Wei, Qingyue, Huang, Shih-Cheng, Shen, Liyue, Rubin, Daniel, Xing, Lei, Zhou, Yuyin
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enabl
Externí odkaz:
http://arxiv.org/abs/2205.08576
Autor:
Zhou, Yuyin, Li, Xianhang, Liu, Fengze, Wei, Qingyue, Chen, Xuxi, Yu, Lequan, Xie, Cihang, Lungren, Matthew P., Xing, Lei
Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method, named Learning
Externí odkaz:
http://arxiv.org/abs/2202.04291
Autor:
Chen, Jieneng, Mei, Jieru, Li, Xianhang, Lu, Yongyi, Yu, Qihang, Wei, Qingyue, Luo, Xiangde, Xie, Yutong, Adeli, Ehsan, Wang, Yan, Lungren, Matthew P., Zhang, Shaoting, Xing, Lei, Lu, Le, Yuille, Alan, Zhou, Yuyin
Publikováno v:
In Medical Image Analysis October 2024 97
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Akademický článek
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