Zobrazeno 1 - 10
of 686
pro vyhledávání: '"Duan Yuping"'
Autor:
Zhi, Yuxing, Guo, Yuan, Yuan, Kai, Wang, Hesong, Xu, Heng, Yao, Haina, Yang, Albert C, Huang, Guangrui, Duan, Yuping
Background: Large language models (LLMs) have seen extraordinary advances with applications in clinical decision support. However, high-quality evidence is urgently needed on the potential and limitation of LLMs in providing accurate clinical decisio
Externí odkaz:
http://arxiv.org/abs/2409.14478
Real-world data often has a long-tailed distribution, where the scarcity of tail samples significantly limits the model's generalization ability. Denoising Diffusion Probabilistic Models (DDPM) are generative models based on stochastic differential e
Externí odkaz:
http://arxiv.org/abs/2409.14313
The unrolling method has been investigated for learning variational models in X-ray computed tomography. However, it has been observed that directly unrolling the regularization model through gradient descent does not produce satisfactory results. In
Externí odkaz:
http://arxiv.org/abs/2401.15663
The convolutional neural network-based methods have become more and more popular for medical image segmentation due to their outstanding performance. However, they struggle with capturing long-range dependencies, which are essential for accurately mo
Externí odkaz:
http://arxiv.org/abs/2401.15307
Autor:
Zhou, Yan-Jie, Liu, Wei, Gao, Yuan, Xu, Jing, Lu, Le, Duan, Yuping, Cheng, Hao, Jin, Na, Man, Xiaoyong, Zhao, Shuang, Wang, Yu
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for
Externí odkaz:
http://arxiv.org/abs/2307.08308
The challenges in recovering underwater images are the presence of diverse degradation factors and the lack of ground truth images. Although synthetic underwater image pairs can be used to overcome the problem of inadequately observing data, it may r
Externí odkaz:
http://arxiv.org/abs/2303.06543
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled
Externí odkaz:
http://arxiv.org/abs/2301.00406
Autor:
Li, Yutong, Duan, Yuping
Due to the development of deep learning-based denoisers, the plug-and-play strategy has achieved great success in image restoration problems. However, existing plug-and-play image restoration methods are designed for non-blind Gaussian denoising such
Externí odkaz:
http://arxiv.org/abs/2211.07286
In the practical applications of computed tomography imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scanning conditions. The noisy incomplete projection data results in the i
Externí odkaz:
http://arxiv.org/abs/2208.00207