CMCRL

KO | EN

gitlite — search

#javascript #hacktoberfest #typescript #android #rust #linux #docker #windows #awesome #kubernetes #awesome-list #pytorch #ios #openai

(★ 68)

The official implementation of “Cross-Modal Causal Representation Learning for Radiology Report Generation” （IEEE T-IP 2025）

CMCRL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# CMCRL

This is the implementation of [Cross-Modal Causal Intervention for Medical Report Generation](https://arxiv.org/pdf/2303.09117.pdf).
It contains the codes of the Radiological Cross-modal Alignment and Reconstruction Enhanced（RadCARE）, and fine-tuning via Visual-Linguistic Causal Intervention (VLCI) on IU-Xray/MIMIC-CXR dataset.

</div>

## 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](https://github.com/cuhksz-nlp/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](https://pan.baidu.com/s/1LKp_XLUVttAkgCHwm6QHpg?pwd=vsg1).
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 |
| CMCRL              | **0.505** | **0.334** | **0.245** | **0.189** |0.456  |**0.397**|**0.204** |

</div>

- 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  |
| CMCRL              | **0.400** | **0.245** | **0.165** | **0.119** | 0.190 | 0.280   |  0.150  | **0.489** | **0.340** | **0.401** |

</div>

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

```
@ARTICLE{11005686,
  author={Chen, Weixing and Liu, Yang and Wang, Ce and Zhu, Jiarui and Li, Guanbin and Liu, Cheng-Lin and Lin, Liang},
  journal={IEEE Transactions on Image Processing}, 
  title={Cross-Modal Causal Representation Learning for Radiology Report Generation}, 
  year={2025},
  volume={34},
  pages={2970-2985},
  doi={10.1109/TIP.2025.3568746}}
```

## Contact
If you have any questions about this code, feel free to reach me (chenwx228@mail2.sysu.edu.cn)

## Acknowledges
We thank [R2Gen](https://github.com/cuhksz-nlp/R2Gen) for their open source works.

CMCRL

CMCRL 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

CMCRL

CMCRL 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README