Summary of Pefomed: Parameter Efficient Fine-tuning Of Multimodal Large Language Models For Medical Imaging, by Jinlong He et al.
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
by Jinlong He, Pengfei Li, Gang Liu, Genrong He, Zhaolin Chen, Shenjun Zhong
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Multimodal large language models (MLLMs) are an advancement in traditional language models, enabling them to tackle challenges that surpass text-based applications. The abstract proposes a parameter-efficient framework for fine-tuning MLLMs on medical visual question answering (Med-VQA) and report generation (MRG) tasks using public benchmark datasets. A novel evaluation metric is introduced, combining human ratings and GPT-4 model assessments. Results show close alignment between semantic similarity assessments using GPT-4 and human annotators, but a discrepancy with conventional lexical similarity measurements, questioning the reliability of these metrics for evaluating generative models in Med-VQA and report generation tasks. Fine-tuned models outperform GPT-4v on medical imaging tasks, highlighting the need for additional fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models to help doctors and researchers work with medical images and text. It’s like a super-smart AI that can understand pictures of organs and generate reports about what it sees. The model is trained on lots of data and tested on two big tasks: recognizing questions about medical images (Med-VQA) and generating reports about those images. The results show that the model does a great job, but we need to be careful when using words to measure its performance. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Gpt » Parameter efficient » Question answering
