Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningHippocampusOut-of-Distribution DetectionSimilar Papers 제목 키워드 기반
Continual Hippocampus Segmentation with Transformers
In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional ar…
Continual LearningHippocampusimage-classificationImage Classification+4NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation
Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt, a Neural Cellular Automata (NCA) based…
BenchmarkingContinual LearningHippocampusWhat is Wrong with Continual Learning in Medical Image Segmentation?
Continual learning protocols are attracting increasing attention from the medical imaging community. In continual environments, datasets acquired under different conditions arrive sequentially; and each is only available…
Continual LearningDiagnosticHippocampusimage-classification+5Distribution-Aware Replay for Continual MRI Segmentation
Medical image distributions shift constantly due to changes in patient population and discrepancies in image acquisition. These distribution changes result in performance deterioration; deterioration that continual learn…
Continual LearningHippocampusImage SegmentationMedical Image Segmentation+3SegReg: Latent Space Regularization for Improved Medical Image Segmentation
Medical image segmentation models are typically optimised with voxel-wise losses that constrain predictions only in the output space. This leaves latent feature representations largely unconstrained, potentially limiting…
Medical Image SegmentationContinual Learning