Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisition characteristics as data and targets may become inconsistent. Continual learning can help to adapt models to the changing environment by training on a continuous data stream. However, continual manual expert labelling of medical imaging requires substantial effort. Thus, ways to use labelling resources efficiently on a well chosen sub-set of new examples is necessary to render this strategy feasible. Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting. The approach automatically recognizes shifts in image acquisition characteristics - new domains -, selects optimal examples for labelling and adapts training accordingly. Labelling is subject to a limited budget, resembling typical real world scenarios. To demonstrate generalizability, we evaluate the effectiveness of our method on three tasks: cardiac segmentation, lung nodule detection and brain age estimation. Results show that the proposed approach outperforms other active learning methods, while effectively counteracting catastrophic forgetting.

翻译：临床常规期间医学成像机的学习因扫描仪规程、硬件或政策的变化而受到影响,导致一系列不同的获取环境。当在初始静态培训集中培训深学习模式时,模型性能和可靠性会因获取特征的变化而受到影响,因为数据和目标可能变得不一致。持续学习有助于通过连续数据流培训使模型适应不断变化的环境。然而,持续人工对医学成像进行专家贴标签需要大量努力。因此,在精心选择的一组子实例上高效使用标签资源的方法对于使这一战略可行是必要的。在这里,我们提出了一个在多扫描仪环境中持续积极学习在医疗成像流上运行的方法。该方法自动承认图像获得特征的变化----新领域----为标签选择最佳范例并相应调整培训。标签受有限预算的限制,将典型的实际情况重新组合为现实情景。为了证明普遍性,我们评估我们的方法在以下三项任务上的有效性:心脏分块、肺结核探测和脑年龄估计。结果显示,拟议的方法优于其他积极学习方法,同时有效防止灾难性的遗忘。