Learning From Scarcity

Abstract

This chapter delves into the transformative synergy of few-shot learning and healthcare, elucidating its impact on medical procedures. Anchored in machine learning fundamentals, it establishes a core framework through a review of algorithms. Addressing challenges of small healthcare datasets, the chapter highlights the pivotal role of few-shot learning. Innovative methods like multimodal integration and federated learning enhance model robustness, offering insights into complex healthcare scenarios. Formal mathematical explanations categorize few-shot learning challenges, opening avenues for a deeper understanding and implementation in medical imaging.

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