Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of...

Medical image analysis is essential for modern diagnostics, as it enables accurate and rapid disease detection. However, traditional deep learning models require large, annotated datasets, which are frequently inaccessible in medical scenarios due to data scarcity, privacy constraints, and excessive labeling costs. Few-Shot Learning (FSL) and Automated Machine Learning (AutoML) have appeared as effective techniques to address these issues. FSL utilizes meta-learning and metric-based strategies enabling models to learn from small samples, while AutoML automates model design and optimization, reducing reliance on expert intervention. Additionally, the integration of domain-specific knowledge, including anatomical priors and clinically relevant features, has demonstrated enhancements in interpretability and diagnostic significance. This mini review offers a structured analysis of FSL, AutoML, and domain-specific knowledge in medical image analysis, emphasizing their potential integration. A critical evaluation of existing literature reveals that most studies use these approaches independently. The review further examines methodological limitations, dataset constraints, and clinical applicability challenges across current studies. Based on these findings, key research gaps are identified, such as the need for domain-informed architecture search, standardized evaluation protocols, and pipelines that use less computer power. Notably, metric-based FSL approaches were more widely used than gradient-based methods due to their stability under limited data conditions. However, the literature is still methodologically fragmented, with FSL, AutoML, and domain-specific knowledge mainly studied separately or in partial combinations. The paper concludes by outlining future research directions toward the development of AutoML-enhanced FSL frameworks integrated with domain-specific knowledge to improve performance, interpretability, and clinical reliability in data-constrained environments.