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 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. Since imaging environments evolve rapidly and can be generated by edge
devices, the algorithm is required to continually learn and adapt to changing
environments, and adjust to low-compute devices. To this end, we developed
three image coreset algorithms to compress and denoise medical images for
selective experience replayed-based lifelong reinforcement learning. We
implemented neighborhood averaging coreset, neighborhood sensitivity-based
sampling coreset, and maximum entropy coreset on full-body DIXON water and
DIXON fat MRI images. All three coresets produced 27x compression with
excellent performance in localizing five anatomical landmarks: left knee, right
trochanter, left kidney, spleen, and lung across both imaging environments.
Maximum entropy coreset obtained the best performance of $11.97\pm 12.02$
average distance error, compared to the conventional lifelong learning
framework's $19.24\pm 50.77$.