Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects

TL;DR

This paper reviews recent advancements in continual learning for medical image analysis, highlighting methods to address data drift, catastrophic forgetting, and model adaptability in evolving medical datasets.

Contribution

It provides a comprehensive survey of continual learning techniques applied to medical imaging, including scenario analysis, evaluation metrics, and applicability across sub-fields.

Findings

Rehearsal and regularization are the most popular continual learning strategies in medical imaging.

Continual learning effectively mitigates catastrophic forgetting in medical image analysis.

Different medical sub-fields benefit from tailored continual learning approaches.

Abstract

Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms. However, when the inference dataset slightly differs from what the model has seen during one-time training, the model performance is greatly compromised. The situation requires restarting the training process using both the old and the new data which is computationally costly, does not align with the human learning process, and imposes storage constraints and privacy concerns. Alternatively, continual learning has emerged as a crucial approach for developing unified and sustainable deep models to deal with new classes, tasks, and the drifting nature of data in non-stationary environments for various application areas. Continual learning techniques enable models to adapt and accumulate knowledge over time, which is essential for maintaining performance on evolving datasets and novel tasks. This systematic review paper provides a comprehensive overview of the state-of-the-art in continual learning techniques applied to medical imaging analysis. We present an extensive survey of existing research, covering topics including catastrophic forgetting, data drifts, stability, and plasticity requirements. Further, an in-depth discussion of key components of a continual learning framework such as continual learning scenarios, techniques, evaluation schemes, and metrics is provided. Continual learning techniques encompass various categories, including rehearsal, regularization, architectural, and hybrid strategies. We assess the popularity and applicability of continual learning categories in various medical sub-fields like radiology and histopathology...