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Most continual learning methods are validated in settings where taskboundaries are clearly defined and task identity information is availableduring training and testing. We explore how such methods perform in atask-agnostic setting that more closely resembles dynamic clinical environmentswith gradual population shifts. We propose ODEx, a holistic solution thatcombines out-of-distribution detection with continual learning techniques.Validation on two scenarios of hippocampus segmentation shows that our proposedmethod reliably maintains performance on earlier tasks without losingplasticity.

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts