A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments

no code implementations • 8 Jun 2023 • Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh

While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs.

reinforcement-learning

Multi-environment lifelong deep reinforcement learning for medical imaging

no code implementations • 31 May 2023 • Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh

Deep reinforcement learning(DRL) is increasingly being explored in medical imaging.

reinforcement-learning

Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging

no code implementations • 12 Mar 2023 • Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh

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 location or device.

Brain Tumor Segmentation Federated Learning +1

Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging

no code implementations • 22 Feb 2023 • Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh

Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.

Brain Tumor Segmentation Tumor Segmentation