Summary of Multimodal Medical Disease Classification with Llama Ii, by Christian Gapp et al.
Multimodal Medical Disease Classification with LLaMA II
by Christian Gapp, Elias Tappeiner, Martin Welk, Rainer Schubert
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 deep learning-based approach to processing and integrating multimodal patient data in medical diagnosis and treatment planning. The authors retrain a transformer-based model on a dataset consisting of 2D chest X-rays and clinical reports from OpenI. They explore different architecture structures with a LLaMA II backbone model, focusing on fusion methods for merging text and vision information. The results show that early fusion of modality-specific features outperforms late fusion, achieving a mean AUC of 97.10% with the best model. This multimodal architecture can be applied to other datasets with minimal effort and has potential applications in medical AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors diagnose patients better. It combines different types of data like pictures, text, and information about a patient’s age and gender. The authors test a special type of computer model that can look at this mixed data and make good predictions about what’s going on with the patient. They find that one way of combining the different types of data works better than another. This could help doctors make more accurate diagnoses, which is important for patients to get the right treatment. |
Keywords
» Artificial intelligence » Auc » Deep learning » Llama » Transformer
