This is the implementation of [GMoD: Graph-driven Momentum Distillation Framework with Active Perception of Disease Severity for Radiology Report Generation] at MICCAI2024.

• einops==0.8.0
• matplotlib==3.7.1
• nltk==3.8.1
• numpy==1.24.2
• opencv_python==4.7.0.72
• pandas==1.5.3
• Pillow==9.4.0
• Pillow==10.3.0
• scikit_learn==1.2.2
• scipy==1.9.1
• timm==0.4.12
• torch==2.0.0+cu118
• torch_geometric==2.3.1
• tqdm==4.65.0

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

We use two datasets (IU X-Ray and MIMIC-CXR) in our paper.

For IU X-Ray, you can download the dataset from here and then put the files in data/iu_xray.

For MIMIC-CXR, you can download the dataset from here and then put the files in data/mimic_cxr.

NOTE: The IU X-Ray dataset is of small size, and thus the variance of the results is large. There have been some works using MIMIC-CXR only and treating the whole IU X-Ray dataset as an extra test set.

After downloading the raw dataset, you need to add count_nounphrase.json and mimic-cxr-2.0.0-chexpert.csv to the . /mimic_cxr/ or . /iu_xray/ directory

Run bash main_train.py to train the model.