metadata
language: en
license: mit
tags:
- electrocardiogram
- multimodal-learning
- zero-shot-learning
- medical-ai
- interpretability
Interpretable Multimodal Zero-Shot ECG Diagnosis (ZETA)
🧠 Overview
We propose ZETA, a zero-shot multimodal framework for ECG diagnosis that aligns signals with structured clinical observations.
Instead of directly predicting diseases, ZETA compares ECG signals with positive and negative clinical evidence, mimicking differential diagnosis.
🖼️ Framework
⚙️ Method
- Structured observations: LLM-generated + expert-validated
- Multimodal alignment: pretrained ECG-text model
- Inference:
- ✅ match with positive observations
- ❌ match with negative observations
Prediction is based on relative evidence strength.
📊 Results
📦 Checkpoint
ZETA/checkpoints/best.pt