Dynamic Memory to Alleviate Catastrophic Forgetting in Continual Learning with Medical Imaging
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Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.
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Continuous monitoring systems detect when models encounter data outside their training distribution or when performance begins to degrade.
“Continuous monitoring systems detect when models encounter data outside their training distribution or when performance begins to degrade [147,148,149,150,151].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [149]
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- Authors
- Perkonigg, M., Hofmanninger, J., Herold, C. J., Brink, J. A., Pianykh, O., Prosch, H., & Langs, G.
- Affiliations
- Medical University of Vienna (AT)
- Harvard University (US)
- Corresponding author
- Langs, Georg
- ORCID
- Venue
- Nature Communications
- Published
- 2021-09-28
- Volume
- 12
- Issue
- 1
- Pages
- 5678-5678
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8479083
- OpenAlex
- W3202253200
- Cited by
- 106
- References
- 61
- Citations by year
- 2026: 24 · 2025: 28 · 2024: 15 · 2023: 23 · 2022: 14 · 2021: 2
- FWCI
- 4.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Boehringer Ingelheim
- European Commission (780495, 765148)
- Austrian Science Fund (P34198, P35189)
- Oesterreichische Nationalbank (18207)
- Novartis Pharmaceuticals Corporation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- catastrophic forgetting, continual learning, dynamic memory, domain shift, diagnostic imaging, cardiac image segmentation, lung nodule detection, magnetic resonance imaging, computed tomography, model updating
- MeSH terms
- Machine Learning, Diagnostic Imaging, Humans, Learning, Lung, Memory, Tomography, X-Ray Computed, Neural Networks, Computer
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