Multi-environment lifelong deep reinforcement learning for medical
  imaging
Guangyao Zheng Shuhao Lai Vladimir Braverman Michael A. Jacobs Vishwa S. Parekh
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 orientations, imaging sequences, and pathologies.
To that end, we developed a Lifelong DRL framework, SERIL to continually learn
new tasks in changing imaging environments without catastrophic forgetting.
SERIL was developed using selective experience replay based lifelong learning
technique for the localization of five anatomical landmarks in brain MRI on a
sequence of twenty-four different imaging environments. The performance of
SERIL, when compared to two baseline setups: MERT(multi-environment-best-case)
and SERT(single-environment-worst-case) demonstrated excellent performance with
an average distance of $9.90\pm7.35$ pixels from the desired landmark across
all 120 tasks, compared to $10.29\pm9.07$ for MERT and $36.37\pm22.41$ for
SERT($p<0.05$), demonstrating the excellent potential for continuously learning
multiple tasks across dynamically changing imaging environments.