Summary of Leveraging Multimodal Models For Enhanced Neuroimaging Diagnostics in Alzheimer’s Disease, by Francesco Chiumento et al.
Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer’s Disease
by Francesco Chiumento, Mingming Liu
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Image and Video Processing (eess.IV)
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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 paper proposes a method for generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset to fine-tune Large Language Models (LLMs) and Vision-Language Models (VLMs) for neuroimaging applications, particularly for Alzheimer’s disease. The approach leverages pre-trained BiomedCLIP and T5 models to generate neurological reports directly from images in the dataset, achieving promising results with BLEU-4, ROUGE-L, and METEOR scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to help doctors diagnose diseases like Alzheimer’s by generating fake medical reports. This can be helpful because it allows researchers to train their AI models on more data, which makes them better at predicting what the doctor will write in a real report. |
Keywords
» Artificial intelligence » Bleu » Gpt » Rouge » T5
