Výsledky vyhledávání - "Zeng, Hongwu"

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RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping

Autor: Huang, Chiyi, Sun, Longwei, Liang, Dong, Liang, Haifeng, Zeng, Hongwu, Zhu, Yanjie

Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base

Externí odkaz: http://arxiv.org/abs/2410.11651

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Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Autor: Xie, Taofeng, Cao, Chentao, Cui, Zhuoxu, Guo, Yu, Wu, Caiying, Wang, Xuemei, Li, Qingneng, Hu, Zhanli, Sun, Tao, Sang, Ziru, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Jin, Qiyu, Zeng, Hongwu, Chen, Guoqing, Wang, Haifeng

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultane

Externí odkaz: http://arxiv.org/abs/2305.03901

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ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis

Autor: Cao, Xu, Ye, Wenqian, Sizikova, Elena, Bai, Xue, Coffee, Megan, Zeng, Hongwu, Cao, Jianguo

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established basel

Externí odkaz: http://arxiv.org/abs/2210.16943

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AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation

Autor: Cao, Xu, Li, Xiaoye, Ma, Liya, Huang, Yi, Feng, Xuan, Chen, Zening, Zeng, Hongwu, Cao, Jianguo

Publikováno v: 31st International Joint Conference on Artificial Intelligence (IJCAI-22). (2022) 700

Movement and pose assessment of newborns lets experienced pediatricians predict neurodevelopmental disorders, allowing early intervention for related diseases. However, most of the newest AI approaches for human pose estimation methods focus on adult

Externí odkaz: http://arxiv.org/abs/2205.05277

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