Awesome

ARL

This is the implementation of Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge at ACMMM-2022.

Table of Contents

• Requirements
• Pre-training
• Downstream Evaluation

Requirements

Run the following command to install the required packages:

pip install -r requirements.txt

Note: ARL involves the knowledge extraction and knowledge integration, which need more packages. Therefore, please be patient to install the environment. :-)

Pre-training

1. Dataset Preparation

Please organize the pre-training datasets as the following structure:

root:[data]
+--pretrain_data
| +--roco
| | +--train
| | +--val
| | +--test
| +--medicat
| | +--net
| | +--release
| +--mimic_cxr
| | +--files
| | +--mimic_cxr_sectioned.csv
| | +--mimic-cxr-2.0.0-split.csv
| | +--mimic-cxr-2.0.0-metadata.csv
| | +--mimic-cxr-2.0.0-chexpert.csv

2. Pre-processing

Run the following command to pre-process the data:

python prepro/prepro_pretraining_data.py

to get the following arrow files:

root:[data]
+--pretrain_arrows
| +--medicat_train.arrow
| +--medicat_val.arrow
| +--medicat_test.arrow
| +--roco_train.arrow
| +--roco_val.arrow
| +--roco_test.arrow
| +--mimic_cxr_train.arrow
| +--mimic_cxr_val.arrow
| +--mimic_cxr_test.arrow

3. Download the initialized weights for pre-training

Download the initialized meter weights here.

4. Pre-training

Now we can start to pre-train the arl model:

bash run_scripts/pretrain_arl.sh

Downstream Evaluation

1. Dataset Preparation

Please organize the fine-tuning datasets as the following structure:

root:[data]
+--finetune_data
| +--melinda
| | +--train.csv
| | +--dev.csv
| | +--test.csv
| | +--melinda_images
| +--slack
| | +--train.json
| | +--validate.json
| | +--test.json
| | +--imgs
| +--vqa_rad
| | +--trainset.json
| | +--valset.json
| | +--testset.json
| | +--images
| +--medvqa_2019
| | +--val
| | +--test
| | +--train

2. Pre-processing

Run the following command to pre-process the data:

python prepro/prepro_finetuning_data.py

to get the following arrow files:

root:[data]
+--finetune_arrows
| +--vqa_vqa_rad_train.arrow
| +--vqa_vqa_rad_val.arrow
| +--vqa_vqa_rad_test.arrow
| +--vqa_slack_train.arrow
| +--vqa_slack_test.arrow
| +--vqa_slack_val.arrow
| +--vqa_medvqa_2019_train.arrow
| +--vqa_medvqa_2019_val.arrow
| +--vqa_medvqa_2019_test.arrow
| +--cls_melinda_train.arrow
| +--cls_melinda_val.arrow
| +--cls_melinda_test.arrow
| +--irtr_roco_train.arrow
| +--irtr_roco_val.arrow
| +--irtr_roco_test.arrow

3. Fine-Tuning

Now you can start to fine-tune the arl model:

bash run_scripts/finetune_arl.sh

Acknowledgement

The code is based on OpenKE, ViLT , METER and MAE. We thank the authors for their open-sourced code and encourage users to cite their works when applicable.

Citations

If ARL is useful for your research, please consider citing:

@inproceedings{chen2022arl,
  title={Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge},
  author={Chen, Zhihong and Li, Guanbin and Wan, Xiang},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  year={2022}
}