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Continual Learning in Medical Imaging: A Survey and Practical Analysis
Qazi, Mohammad Areeb Hashmi, Anees Ur Rehman Sanjeev, Santosh Almakky, Ibrahim Saeed, Numan Gonzalez, Camila Yaqub, Mohammad
Qazi, Mohammad Areeb
Hashmi, Anees Ur Rehman
Sanjeev, Santosh
Almakky, Ibrahim
Saeed, Numan
Gonzalez, Camila
Yaqub, Mohammad
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3785663.pdf
Adobe PDF, 863.35 KB
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Computer Vision
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Journal article
Date
2025
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English
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Abstract
Deep Learning has shown great success in reshaping medical imaging, yet it faces several challenges hindering widespread application. Distribution shifts in the continuously evolving data stream and catastrophic forgetting are two barriers that considerably increase the performance gap between research and applications. Continual Learning offers promise in addressing these hurdles by enabling the sequential acquisition of new knowledge without forgetting previous information. In this survey, we comprehensively review the recent literature on continual learning in the medical domain, highlight recent trends, and point out several practical issues. Specifically, we survey the continual learning studies on classification, segmentation, detection, and other related tasks in the medical domain and develop a taxonomy for the reviewed studies. We also critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions. We hope that this survey provides researchers with a useful overview of the developments in the field and further increases engagement with this topic within the community. To keep up with the fast-paced advancements in the field, we will routinely update the repository with the latest relevant papers at https://github.com/BioMedIA-MBZUAI/awesome-cl-in-medical.
Citation
M. A. Qazi et al., “Continual Learning in Medical Imaging: A Survey and Practical Analysis,” ACM Comput Surv, Nov. 2024, doi: 10.1145/3785663
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ACM Computing Surveys
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Association for Computing Machinery