面向医学影像的多环境终身深度强化学习
Multi-environment lifelong deep reinforcement learning for medical imaging
- Rice University（莱斯大学）
- The Johns Hopkins University（约翰斯·霍普金斯大学）
- McGovern Medical School, UTHealth Houston（休斯顿UTHealth麦戈文医学院）
- Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
- University of Maryland Medical Intelligent Imaging (UM2ii)（马里兰大学医学智能成像中心）
- University of Maryland School of Medicine（马里兰大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出终身深度强化学习框架SERIL，利用选择性经验回放技术在二十四个脑部MRI成像环境中持续定位五个解剖标志点，在120个任务上显著优于多环境最佳和单环境最差基线，验证了动态医学影像环境中持续学习多个任务的有效性。
AI中文摘要：
深度强化学习（DRL）在医学影像领域的探索日益增多。然而，医学影像任务的环境在成像方位、成像序列和病理方面持续演变。为此，我们开发了一个终身DRL框架SERIL，用于在不断变化的成像环境中持续学习新任务而不发生灾难性遗忘。SERIL基于选择性经验回放的终身学习技术开发，用于在二十四个不同成像环境序列中对脑部MRI中的五个解剖标志点进行定位。将SERIL的性能与两种基线设置进行比较：MERT（多环境最佳情况）和SERT（单环境最差情况），结果表明SERIL表现出色，在所有120个任务中与期望标志点的平均距离为$9.90\pm7.35$像素，而MERT为$10.29\pm9.07$，SERT为$36.37\pm22.41$（$p<0.05$），展示了在动态变化的成像环境中持续学习多个任务的巨大潜力。
英文摘要：
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.
