22 citations · 26 across the 3 of their papers we have counts for
4 papers
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environmen…
Multi-environment lifelong deep reinforcement learning for medical imaging
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging…
Cross-Domain Federated Learning in Medical Imaging
Vishwa S Parekh, Shuhao Lai, Vladimir Braverman +4
Federated learning is increasingly being explored in the field of medical imaging to train deep learning models on large scale datasets distributed across different data centers wh…
A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization
Shu-Zhen Lai, Hou-Biao Li, Zu-Tao Zhang
As we all known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing and signal processing et…