Zobrazeno 1 - 10
of 5 095
pro vyhledávání: '"Peirong, An"'
Autor:
Hu, Xiaoling, Gopinath, Karthik, Liu, Peirong, Hoffmann, Malte, Van Leemput, Koen, Puonti, Oula, Iglesias, Juan Eugenio
Over recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human neuroimaging with magnetic resonance imaging (MRI). However, the uncertainty estimation ass
Externí odkaz:
http://arxiv.org/abs/2410.09299
Multivariate elliptically-contoured distributions are widely used for modeling correlated and non-Gaussian data. In this work, we study the kurtosis of the elliptical model, which is an important parameter in many statistical analysis. Based on U-sta
Externí odkaz:
http://arxiv.org/abs/2408.12131
Classical Chinese is a gateway to the rich heritage and wisdom of ancient China, yet its complexities pose formidable comprehension barriers for most modern people without specialized knowledge. While Large Language Models (LLMs) have shown remarkabl
Externí odkaz:
http://arxiv.org/abs/2407.03937
Existing work implementing comparative reconstruction of ancestral languages (proto-languages) has usually required full supervision. However, historical reconstruction models are only of practical value if they can be trained with a limited amount o
Externí odkaz:
http://arxiv.org/abs/2406.05930
Document image restoration is a crucial aspect of Document AI systems, as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently, leading to intricate syste
Externí odkaz:
http://arxiv.org/abs/2405.04408
Publikováno v:
International Conference on Learning Representations 2024
Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to sub
Externí odkaz:
http://arxiv.org/abs/2404.12091
Autor:
Zhang, Yuyi, Zhu, Yuanzhi, Peng, Dezhi, Zhang, Peirong, Yang, Zhenhua, Yang, Zhibo, Yao, Cong, Jin, Lianwen
Text recognition, especially for complex scripts like Chinese, faces unique challenges due to its intricate character structures and vast vocabulary. Traditional one-hot encoding methods struggle with the representation of hierarchical radicals, reco
Externí odkaz:
http://arxiv.org/abs/2403.13761
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acq
Externí odkaz:
http://arxiv.org/abs/2403.06227
Autor:
Laso, Pablo, Cerri, Stefano, Sorby-Adams, Annabel, Guo, Jennifer, Mateen, Farrah, Goebl, Philipp, Wu, Jiaming, Liu, Peirong, Li, Hongwei, Young, Sean I., Billot, Benjamin, Puonti, Oula, Sze, Gordon, Payabavash, Sam, DeHavenon, Adam, Sheth, Kevin N., Rosen, Matthew S., Kirsch, John, Strisciuglio, Nicola, Wolterink, Jelmer M., Eshaghi, Arman, Barkhof, Frederik, Kimberly, W. Taylor, Iglesias, Juan Eugenio
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods requir
Externí odkaz:
http://arxiv.org/abs/2312.05119
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated modalities -- notably magnetic resonance (MR) imaging, where performance
Externí odkaz:
http://arxiv.org/abs/2311.16914