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[EMNLP'24] RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

We tackle the challenge of improving factual accuracy in Medical Large Vision Language Models (Med-LVLMs) using our novel approach, RULE. Despite their promise, Med-LVLMs often generate responses misaligned with established medical facts. RULE addresses this with two key strategies: 1) Calibrated selection of retrieved contexts to control factuality risk. 2) Fine-tuning models using a preference dataset to balance reliance on inherent knowledge and retrieved contexts. Our method achieves a 20.8% improvement in factual accuracy across three medical VQA datasets.

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models [Paper] <br>

🌟 Requirements

1. Clone this repository and navigate to RULE folder

git clone https://github.com/richard-peng-xia/RULE.git
cd RULE

2. Install Package: Create conda environment

conda create -n RULE python=3.10 -y
conda activate RULE
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install trl

3. Download the required model checkpoints LLaVA-Med-1.5 from huggingface.

4. For all the medical datasets, you need firstly apply for the right of access and then download the dataset.

• MIMIC-CXR
• IU-Xray (Thanks to R2GenGPT for sharing the file)
• Harvard-FairVLMed

📖 Data Description

We provide a corresponding json or jsonl file for each dataset, including the image path, question, answer, and original report.

• Training: The data used to train the retriever and fine-tune the Med-LVLM are located in data/training/retriever and data/training/alignment respectively.

• Test: All the test data for Med-LVLMs is placed under data/test.

🚀 Training

• The training code of Direct Preference Optimization is at llava/train/train_dpo.py.
• The relevant script can be found at scripts/run_dpo.sh

🥖 Retrieve

Use retrieve_clip.sh to retrieve reports for VQA task. The script uses Harvard-FairVLMed dataset as an example. Make sure to specify the necessary data paths and the saving location.

🛠️ Inference

• For test dataset inference, you need to specify the following arguments.

python llava/eval/model_vqa_{dataset}.py \
    --model-base 'path/to/llava-med-1.5' \
    --model-path 'path/to/lora_weights' \
    --question-file 'path/to/question_file.json' \
    --image-folder 'path/to/test_images' \
    --answers-file 'path/to/output_file.json'

• The written script is at scripts/inference.sh. Before that, you need to set the correct path of data and checkpoints in your script.

📚 Citation

@article{xia2024rule,
  title={RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models},
  author={Xia, Peng and Zhu, Kangyu and Li, Haoran and Zhu, Hongtu and Li, Yun and Li, Gang and Zhang, Linjun and Yao, Huaxiu},
  journal={arXiv preprint arXiv:2407.05131},
  year={2024}
}

🙏 Acknowledgement

We use code from LLaVA-Med, POVID, CARES. We thank the authors for releasing their code.