Awesome

The official GitHub repository of the AAAI-2024 paper "Bootstrapping Large Language Models for Radiology Report Generation".

Reference

If our work is helpful to your research, please cite our paper:

@inproceedings{chang2024bootstrapping,
  author       = {Chang Liu and
                  Yuanhe Tian and
                  Weidong Chen and
                  Yan Song and
                  Yongdong Zhang},
  editor       = {Michael J. Wooldridge and
                  Jennifer G. Dy and
                  Sriraam Natarajan},
  title        = {Bootstrapping Large Language Models for Radiology Report Generation},
  booktitle    = {AAAI},
  pages        = {18635--18643},
  year         = {2024},
}

Getting Started

1. Before you run the code, you need to create a virtual environment and activate it via the following command:

conda env create -f environment.yaml
conda activate venv

2. Once the virtual environment is created, you need to download the LLM model weights following the instruction in MiniGPT-4. Once the model weights are downloaded, you need to modify some configuration files:

• minigpt4/models/minigpt4-7b.yaml: line 16 with the path of Vicuna 7b model weights.
• minigpt4/models/minigpt4.yaml: line 16 with the path of Vicuna 13b model weights.

3. You need to download the dataset from the official websites of IU X-Ray and MIMIC-CXR. Once the datasets are ready, you need to modify some configuration files:

• minigpt4/configs/datasets/iuxray/align.yaml: line 5 with the path of pre-training dataset.
• minigpt4/configs/datasets/iuxray/generate_then_refine.yaml: line 5 with the path of IU X-Ray dataset, line 6 with the path of public medical corpora.
• minigpt4/configs/datasets/mimic/align.yaml: line 5 with the path of pre-training dataset.
• minigpt4/configs/datasets/mimic/generate_then_refine.yaml: line 5 with the path of MIMIC-CXR dataset, line 6 with the path of public medical corpora.

Training

1. Pre-training. We recommend you to follow the instructions below to pre-train MiniGPT-4 on MIMIC-CXR.

(1) Modify the configuration files.

• train_configs/stage1/config.yaml: line 12 with the path of the linear projection layer of MiniGPT-4, line 59 with the output path.

(2) Run the following command lines to pre-train MiniGPT-4 on MIMIC-CXR.

python train.py --cfg-path train_configs/stage1/config.yaml

If you need to reduce the memory usage, we recommend you to use the first stage strategy of ZeRO optimizer. Run the following command lines to pre-train MiniGPT-4 on MIMIC-CXR with a lower memory usage.

deepspeed --nproc-per-gpu NUM_GPUS --master-port MASTER_PORT train.py --cfg-path train_configs/stage1/config.yaml use_zero_optimizer --deepspeed_config train_configs/stage1/zero.json

You can download our pre-trained model weights from here.

2. Fine-tuning. We recommend you to follow the instructions below to fine-tune MiniGPT-4 on IU X-Ray and MIMIC-CXR.

(1) Modify the configuration files. Herein, we take the IU X-Ray configuration as an example.

• train_configs/stage2/iuxray/config.yaml: line 11 with the path of the linear projection layer of pre-trained MiniGPT-4 on MIMIC-CXR, line 56 with the output path.

(2) Run the following command lines to fine-tune MiniGPT-4.

python train.py --cfg-path train_configs/stage2/iuxray/config.yaml

Our codebase supports ZeRO to reduce the memory usage. You can run the following command lines with ZeRO.

deepspeed --nproc-per-gpu NUM_GPUS --master-port MASTER_PORT train.py --cfg-path train_configs/stage2/iuxray/config.yaml use_zero_optimizer --deepspeed_config train_configs/stage2/iuxray/zero.json

You can download our fine-tuned model weights from here.

Inference

Run the following command lines to generate radiology reports.

python generate_reports.py \
--cfg-path configs/eval_configs/eval.yaml \
--gpu-id GPU_IDS \
--image_path IMAGE_PATH \
--annotations ANNOTATIONS_PATH_OF_IUXRAY_OR_MIMIC \
--checkpoint PATH_TO_PRETRAINED_MODEL_WEIGHTS \

Acknowledgement

This GitHub repository is heavily built based on the MiniGPT-4 repository. Thanks to the authors for their great work!