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README.md
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Example: 5_atrial_fibrillation_yes represents Template ID *5*, attribute *"atrial fibrillation"*, answer *"yes"*.
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<img src="img/FSL_ECG_QAMeta-Learning.png" alt="An example of n way 1 shot and how to utilize in Meta-learning."
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To utilize this dataset, the authors propose a novel multimodal meta-learning framework that integrates a frozen ECG encoder, a frozen language model (e.g., LLaMA or Gemma), and a trainable cross-modal fusion module. This setup effectively aligns ECG signals with natural language queries to enable accurate clinical question answering.
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Example: 5_atrial_fibrillation_yes represents Template ID *5*, attribute *"atrial fibrillation"*, answer *"yes"*.
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<img src="img/FSL_ECG_QAMeta-Learning.png" alt="An example of n way 1 shot and how to utilize in Meta-learning." style="max-width:100%; height:auto;">
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To utilize this dataset, the authors propose a novel multimodal meta-learning framework that integrates a frozen ECG encoder, a frozen language model (e.g., LLaMA or Gemma), and a trainable cross-modal fusion module. This setup effectively aligns ECG signals with natural language queries to enable accurate clinical question answering.
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