lllsy12138 / organ-aware

Requirements

• torch==1.7.1
• torchvision==0.6.1
• opencv-python==4.4.0.42
• java-1.8.0

Datasets

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

Run on IU X-Ray

Run bash run_iu_xray.sh to train a model on the IU X-Ray data.

Run on MIMIC-CXR

Run bash run_mimic_cxr.sh to train a model on the MIMIC-CXR data.

Explanation of each part

models: This contains the code for network models. The two datasets require different numbers of input images, indicated by _iu_xray and _mimic_cxr suffixes. modules: Implementation of various internal modules of the network. pycocoevalcap: Code for evaluation metrics. main.py: The main function. The training_setting allows for the selection of a specific model. infer.py: Script for outputting generated reports. prepare_data.py: Script for generating annotations for different regions. XX.sh: Command execution scripts.

preprocess

Before training, first run prepare_data.py to generate a structured annotation.json file with the reports.