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 $19.24\pm 50.77$.

翻译：尽管深度强化学习在医学影像领域已得到广泛研究，但这些模型的训练与部署通常需要高性能GPU。由于影像环境快速演变且可由边缘设备生成，算法需要持续学习并适应变化的环境，同时适配低算力设备。为此，我们提出了三种图像核心集算法，用于对选择性经验回放型终身强化学习的医学图像进行压缩与去噪。我们在全身DIXON水像与DIXON脂肪像MRI图像上分别实现了邻域平均核心集、基于邻域灵敏度的采样核心集以及最大熵核心集。三种核心集均实现了27倍压缩，在两种影像环境下对左膝、右转子、左肾、脾脏和肺部五个解剖标志的定位中展现出优异性能。其中最大熵核心集取得了最佳性能，平均距离误差为$11.97\pm 12.02$，而传统终身学习框架的误差为$19.24\pm 50.77$。