Summary of C-melt: Contrastive Enhanced Masked Auto-encoders For Ecg-language Pre-training, by Manh Pham et al.
C-MELT: Contrastive Enhanced Masked Auto-Encoders for ECG-Language Pre-Training
by Manh Pham, Aaqib Saeed, Dong Ma
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework called C-MELT that integrates Electrocardiogram (ECG) signals with their accompanying textual reports for enhanced clinical diagnostics. The integration faces challenges due to modality disparities and the scarcity of labeled data, but C-MELT addresses these issues by pre-training ECG and text data using a contrastive masked auto-encoder architecture. The framework combines generative and discriminative capabilities through masked modality modeling, specialized loss functions, and improved negative sampling strategies. Experimental results on five public datasets demonstrate that C-MELT outperforms existing methods, achieving significant increases in linear probing and zero-shot performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computer science to help doctors better diagnose heart problems by combining two types of data: heartbeat readings (ECG) and written reports from patients. Right now, it’s hard for computers to understand both kinds of information at the same time, but this new framework called C-MELT makes it easier. It uses a special kind of machine learning that combines different pieces of information to make better predictions. |
Keywords
» Artificial intelligence » Encoder » Machine learning » Zero shot
