In the realm of medical imaging, the scarcity of annotated data has long posed challenges for deep learning models, which typically require extensive labeled datasets to function effectively. Few-shot learning (FSL) has emerged as a promising solution, allowing models to learn from a minimal number of examples. A comprehensive review of 80 studies published between 2018 and 2023 highlights the significant role of meta-learning in FSL applications within medical imaging. Meta-learning, or "learning to learn," equips models to adapt quickly to new tasks with limited data, thereby enhancing diagnostic accuracy and efficiency. This approach has been particularly beneficial in areas such as disease detection and classification, where annotated data is often limited. pubmed.ncbi.nlm.nih.gov
The integration of FSL techniques in medical imaging has led to notable advancements in model robustness and adaptability. By leveraging meta-learning strategies, models can generalize from a few examples, reducing the reliance on large annotated datasets. This not only accelerates the development of diagnostic tools but also makes them more accessible in resource-constrained settings. The systematic review underscores the importance of establishing standardized methodological pipelines for FSL in medical imaging, providing a foundation for future research and clinical applications. pubmed.ncbi.nlm.nih.gov