Summary of More: Multi-modal Contrastive Pre-training with Transformers on X-rays, Ecgs, and Diagnostic Report, by Samrajya Thapa et al.
MoRE: Multi-Modal Contrastive Pre-training with Transformers on X-Rays, ECGs, and Diagnostic Report
by Samrajya Thapa, Koushik Howlader, Subhankar Bhattacharjee, Wei le
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 The novel Multi-Modal Contrastive Pre-training Framework combines X-rays, electrocardiograms (ECGs), and radiology/cardiology reports to enhance diagnostic accuracy and facilitate patient assessments. The approach leverages transformers to encode these diverse modalities into a unified representation space. By utilizing LoRA-Peft to reduce trainable parameters in the LLM, incorporating linear attention dropping strategy in the Vision Transformer (ViT) for smoother attention, and providing multimodal attention explanations and retrieval, this framework achieves state-of-the-art performance on several medical datasets, including Mimic-IV, CheXpert, Edema Severity, and PtbXl. The proposed methodology effectively aligns modality-specific features into a coherent embedding, supporting various downstream tasks such as zero-shot classification and multimodal retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special way to combine different types of medical information like X-rays, heart tests, and doctor reports. It uses powerful computer tools called transformers to help doctors make better diagnoses and understand patients better. The new method is really good at recognizing patterns in this mixed information and does even better than other methods on several important medical tasks. |
Keywords
» Artificial intelligence » Attention » Classification » Embedding » Lora » Multi modal » Vision transformer » Vit » Zero shot
