53 citations · 136 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman +1
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single locatio…
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Guangyao Zheng, Samson Zhou, Vladimir Braverman +2
Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experienc…