通过锐度感知元持续学习迈向通用医学图像配准
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning
AI总结：
为解决医学图像配准中的分布偏移与数据收集挑战，本文提出锐度感知元持续学习方法（SAMCL），通过结合经验回放与元学习缓解灾难性遗忘，在四个数据集上实现通用3D医学图像配准，性能优于传统训练策略。
摘要：
当前医学图像配准的深度学习方法通常面临分布偏移和数据收集的挑战，阻碍了现实世界的部署。相比之下，通用医学图像配准旨在同时对广泛的临床相关任务执行配准，因此在临床应用中具有巨大潜力。本文通过提出一种持续学习方法，首次尝试在序列学习场景中实现通用3D医学图像配准的目标。具体而言，我们利用带有经验回放的元学习来缓解灾难性遗忘问题。为了提升元持续学习的泛化能力，我们进一步提出了锐度感知元持续学习（SAMCL）。我们在持续学习设置下的四个数据集上验证了方法的有效性，包括脑部MR、腹部CT、肺部CT以及腹部MR-CT图像对。结果表明，SAMCL在实现通用图像配准方面具有潜力，其表现优于或与普通的序列或多任务集中训练策略相当。源代码将在https://github.com/xzluo97/Continual-Reg提供。
英文摘要：
Current deep learning approaches in medical image registration usually face the challenges of distribution shift and data collection, hindering real-world deployment. In contrast, universal medical image registration aims to perform registration on a wide range of clinically relevant tasks simultaneously, thus having tremendous potential for clinical applications. In this paper, we present the first attempt to achieve the goal of universal 3D medical image registration in sequential learning scenarios by proposing a continual learning method. Specifically, we utilize meta-learning with experience replay to mitigating the problem of catastrophic forgetting. To promote the generalizability of meta-continual learning, we further propose sharpness-aware meta-continual learning (SAMCL). We validate the effectiveness of our method on four datasets in a continual learning setup, including brain MR, abdomen CT, lung CT, and abdomen MR-CT image pairs. Results have shown the potential of SAMCL in realizing universal image registration, which performs better than or on par with vanilla sequential or centralized multi-task training strategies.The source code will be available from https://github.com/xzluo97/Continual-Reg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
