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About this paper
Chenqi Li, Timothy Denison, Tingting Zhu
Researchers from the University of Oxford provide a comprehensive survey of few-shot learning methods tailored for biomedical time series data, categorizing 55 distinct approaches into five main types: data-based, metric-based, model-based, optimization-based, and hybrid. The work analyzes each method's application, clinical benefits, and limitations, addressing the challenge of limited labeled data in healthcare.
Key insights
- Few-shot learning presents a promising paradigm for overcoming data scarcity, inter-subject variability, and privacy concerns in biomedical time series analysis.
- The diverse FSL methods can be systematically categorized based on where they introduce modifications: data, metric, model, or optimization, often with hybrid approaches.
- Understanding the strengths and limitations of each FSL category is crucial for selecting appropriate methods for specific biomedical time series challenges, from diagnosis to personalized monitoring.
Summary by alphaXiv.
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