论文标题
任务不合时宜的持续海马分段，用于平稳种群转移
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
论文作者
论文摘要
大多数持续学习方法都在明确定义任务界限并在培训和测试过程中可用的任务标识信息的设置中验证。我们探讨了这些方法在任务无关的环境中的性能，该环境与逐渐变化的动态临床环境更相似。我们提出了Odex，这是一种整体解决方案，将分布外检测与持续学习技术相结合。在两种情况下，海马细分方案的验证表明，我们提出的方法可靠地维持早期任务的性能而不会失去可塑性。
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.
