Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
This addresses the challenge of adapting continual learning methods to dynamic clinical environments, representing an incremental improvement over existing approaches.
The paper tackled the problem of continual learning in task-agnostic settings with gradual population shifts, proposing ODEx, which maintained performance on earlier tasks without losing plasticity, as validated on hippocampus segmentation scenarios.
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.