mlsanityData to 7 Oct 2026

Distribution-Aware Replay for Continual MRI Segmentation

arXiv 2407.21216 ↗First posted 30 Jul 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

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 learning aims to alleviate. However, only adaptation with data rehearsal strategies yields practically desirable performance for medical image segmentation. Such rehearsal violates patient privacy and, as most continual learning approaches, overlooks unexpected changes from out-of-distribution instances. To transcend both of these challenges, we introduce a distribution-aware replay strategy that mitigates forgetting through auto-encoding of features, while simultaneously leveraging the learned distribution of features to detect model failure. We provide empirical corroboration on hippocampus and prostate MRI segmentation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

22 of 37 references.

References · 22

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10 more
  1. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  2. 2019Diagnosing and Enhancing VAE Modelscited by 192
  3. 2020Modeling the Background for Incremental Learning in Semantic Segmentationcited by 189
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  6. 2020A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learningcited by 86
  7. 2020What is Wrong with Continual Learning in Medical Image Segmentation?cited by 25
  8. 2022Continual Hippocampus Segmentation with Transformerscited by 11
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  10. 2023Continual atlas-based segmentation of prostate MRIcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustnesscited by 1
  2. 2026What to Preserve, Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentationcited by 0
  3. 2026Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunitiescited by 0