In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings or administering of contrast agents are examples that influence image content independent of the scanned biology. Such domain and task shifts limit the applicability of machine learning algorithms in the clinical routine by rendering models obsolete over time. Here, we address the problem of data shifts in a continuous learning scenario by adapting a model to unseen variations in the source domain while counteracting catastrophic forgetting effects. Our method uses a dynamic memory to facilitate rehearsal of a diverse training data subset to mitigate forgetting. We evaluated our approach on routine clinical CT data obtained with two different scanner protocols and synthetic classification tasks. Experiments show that dynamic memory counters catastrophic forgetting in a setting with multiple data shifts without the necessity for explicit knowledge about when these shifts occur.

本研究介绍了一种使用动态内存来适应医学影像中不断变化的数据漂移及应对灾难性遗忘效应的机器学习算法，并在两种不同扫描协议的临床CT数据和合成分类任务中进行了实验验证。实验表明，动态内存能有效对抗多个数据漂移设置下的灾难性遗忘，并不需要显式的知识来识别这些漂移发生的时间。

在连续学习中通过动态内存缓解灾难性遗忘