rongxinglin / vlci

Visual-Linguistic Causal Intervention for Radiology Report Generation

VLCI

This is the implementation of Cross-Modal Causal Intervention for Medical Report Generation. It contains the codes of the Visual-Linguistic Pre-training (VLP), and fine-tuning via Visual-Linguistic Causal Intervention (VLCI) on IU-Xray/MIMIC-CXR dataset.

Requirements

All the requirements are listed in the requirements.yaml file. Please use this command to create a new environment and activate it.

conda env create -f requirements.yaml
conda activate mrg

Preparation

1. Datasets: You can download the dataset via data/datadownloader.py, or download from the repo of R2Gen. Then, unzip the files into data/iu_xray and data/mimic_cxr, respectively.
2. Models: We provide the well-trained models of VLCI for inference, and you can download from here.
3. Please remember to change the path of data and models in the config file (config/*.json).

Evaluation

• For VLCI on IU-Xray dataset

python main.py -c config/iu_xray/vlci.json

Model	B@1	B@2	B@3	B@4	C	R	M
R2Gen	0.470	0.304	0.219	0.165	/	0.371	0.187
CMCL	0.473	0.305	0.217	0.162	/	0.378	0.186
PPKED	0.483	0.315	0.224	0.168	0.351	0.376	0.190
CA	0.492	0.314	0.222	0.169	/	0.381	0.193
AlignTransformer	0.484	0.313	0.225	0.173	/	0.379	0.204
M2TR	0.486	0.317	0.232	0.173	/	0.390	0.192
MGSK	0.496	0.327	0.238	0.178	0.382	0.381	/
RAMT	0.482	0.310	0.221	0.165	/	0.377	0.195
MMTN	0.486	0.321	0.232	0.175	0.361	0.375	/
DCL	/	/	/	0.163	0.586	0.383	0.193
VLCI	0.505	0.334	0.245	0.189	0.456	0.397	0.204

• For VLCI on MIMIC-CXR dataset

python main.py -c config/mimic_cxr/vlci.json

Model	B@1	B@2	B@3	B@4	C	R	M	CE-P	CE-R	CE-F1
R2Gen	0.353	0.218	0.145	0.103	/	0.277	0.142	0.333	0.273	0.276
CMCL	0.334	0.217	0.140	0.097	/	0.281	0.133	/	/	/
PPKED	0.360	0.224	0.149	0.106	0.237	0.284	0.149	/	/	/
CA	0.350	0.219	0.152	0.109	/	0.283	0.151	0.352	0.298	0.303
AlignTransformer	0.378	0.235	0.156	0.112	/	0.283	0.158	/	/	/
M2TR	0.378	0.232	0.154	0.107	/	0.272	0.145	0.240	0.428	0.308
MGSK	0.363	0.228	0.156	0.115	0.203	0.284	/	0.458	0.348	0.371
RAMT	0.362	0.229	0.157	0.113	/	0.284	0.153	0.380	0.342	0.335
MMTN	0.379	0.238	0.159	0.116	/	0.283	0.161	/	/	/
DCL	/	/	/	0.109	0.281	0.284	0.150	0.471	0.352	0.373
VLCI	0.400	0.245	0.165	0.119	0.190	0.280	0.150	0.489	0.340	0.401

Citation

If you use this code for your research, please cite our paper.

@misc{chen2023crossmodal,
      title={Cross-Modal Causal Intervention for Medical Report Generation}, 
      author={Weixing Chen and Yang Liu and Ce Wang and Jiarui Zhu and Guanbin Li and Liang Lin},
      year={2023},
      eprint={2303.09117},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contact

If you have any question about this code, feel free to reach me ([email protected])

Acknowledges

We thank R2Gen for their open source works.