...
机译:基于深度学习的多模态融合实现快速MR重建
Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai 200052, Peoples R China;
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA|Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA;
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA|Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA;
Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai 200052, Peoples R China;
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA|Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA;
Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai 200052, Peoples R China;
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27515 USA|Univ N Carolina, BRIC, Chapel Hill, NC 27515 USA|Korea Univ, Dept Brain & Cognit Engn, Seoul, South Korea;
Deep learning; dense block; fast MR reconstruction; multi-model fusion;
机译:基于深度学习的压缩超快摄影的图像重建
机译:基于深度学习的图像分类的金属粉床融合的质量保证
机译:基于深度学习的图像分类的金属粉床融合的质量保证
机译:提高基于深学习的图像重建的鲁棒性
机译:多模态传感器融合:最优性的原则方法
机译:Hahn-PCNN-CNN:用于临床诊断的端到端多模态脑医学图像融合框架
机译:接收器设计,用于更快的信令:基于深度学习的架构