Awesome

Official pytorch implementation of “X-RGen”

This repo holds the Pytorch implementation of X-RGen:<br />

[ACCV2024 (oral)] Act Like a Radiologist: Radiology Report Generation across Anatomical Regions

Usage

• Create a new conda environment

conda create --name xrgen python=3.7
source activate xrgen

• Clone this repo

git clone https://github.com/YtongXie/X-RGen.git
cd X-RGen

Installation

• Install packages

pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt

cd pycocoevalcap
Download the pycocoevalcap package from [here](https://drive.google.com/drive/folders/1WFxcn2G2bUG-bp7pMEXKxrFWyk1rZmbh?usp=drive_link) and place them in the `pycocoevalcap` directory.

Download our X-RGen weights

You can download the models we trained for our dataset from here.

Data Preparation

• We will not disclose our private data in order to protect privacy. To utilize our code, please format your data according to the specifications outlined below. <br/>
• Put the image data under data/images/ should be like:

      ├── CXR2384_IM-0942/
          ├── 0.png
          ├── 1.png
      ├── CXR2926_IM-1328/
          ├── 0.png
          ├── 1.png

• Put the report data under data/annotation.json should be like the example in data/annotation_example.json

Training

• Please download the pre-trained weights for MedCLIP from here and place them in the models directory.
• Run

python main_train_XRGen.py --batch_size 192 --save_dir results/XRGen

to train a model on your data.

Inference

• Run

python main_test_XRGen.py --batch_size 192 --save_dir results/XRGen --load results/XRGen/model_best.pth

for inference.

Acknowledgements

Part of codes are reused from the R2Gen and MedCLIP for medical visual-language pre-training. Thanks to all authors from R2Gen and MedCLIP.

Contact

Yutong Xie (yutong.xie678@gmail.com)