Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Chen, Xiaoran"'
Autor:
Peng, Zhiheng, Zhao, Kai, Chen, Xiaoran, Ma, Li, Xia, Siyu, Fan, Changjie, Shang, Weijian, Jing, Wei
Efficient, accurate and low-cost estimation of human skeletal information is crucial for a range of applications such as biology education and human-computer interaction. However, current simple skeleton models, which are typically based on 2D-3D joi
Externí odkaz:
http://arxiv.org/abs/2409.01555
Autor:
Chen, Xiaoran
Context. Image enhancement algorithms can be used to enhance the visual effects of images in the field of human vision. So can image enhancement algorithms be used in the field of computer vision? The convolutional neural network, as the most powerfu
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18523
Autor:
Liu, Yihong, McLeod, John A., Chang, Lo-Yueh, Chang, Chung-Kai, Jiang, Yingying, Wang, Zhiqiang, Lefebvre, Amy, Chen, Xiaoran, Liu, Lijia
Publikováno v:
In Materials Today Communications March 2024 38
Autor:
Volokitin, Anna, Erdil, Ertunc, Karani, Neerav, Tezcan, Kerem Can, Chen, Xiaoran, Van Gool, Luc, Konukoglu, Ender
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Aut
Externí odkaz:
http://arxiv.org/abs/2007.04780
Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention
Externí odkaz:
http://arxiv.org/abs/2005.00031
Publikováno v:
In Biochemistry and Biophysics Reports July 2023 34
Akademický článek
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Recently, increasing attention has been drawn to training semantic segmentation models using synthetic data and computer-generated annotation. However, domain gap remains a major barrier and prevents models learned from synthetic data from generalizi
Externí odkaz:
http://arxiv.org/abs/1812.05040
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributions in imaging data. These new models in medical image computing have i
Externí odkaz:
http://arxiv.org/abs/1806.05452
Autor:
Chen, Xiaoran, Konukoglu, Ender
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can d
Externí odkaz:
http://arxiv.org/abs/1806.04972