Update README.md

README.md

@@ -6,14 +6,14 @@ tags:
 - BenchX
 ---
 
-# M-FLAG Checkpoint Model Card
+# REFERS Checkpoint Model Card
 
-A retrained M-FLAG model for benchmarking medical vision-language pre-training methods within the BenchX framework.
+A retrained REFERS model for benchmarking medical vision-language pre-training methods within the BenchX framework.
 
 ## Model Details
-- **Model Type**: M-FLAG
-- **Architecture**: ResNet-50 image encoder and CXR-BERT text encoder
-- **Original Papers**: [M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization](https://arxiv.org/abs/2307.08347)
+- **Model Type**: REFERS
+- **Architecture**: Vit-Base image encoder and BERT text encoder
+- **Original Papers**: [Generalized Radiograph Representation Learning via Cross-Supervision Between Images and Free-Text Radiology Reports](https://arxiv.org/abs/2111.03452)
 - **Benchmark Paper**: [BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays](https://arxiv.org/abs/2410.21969)
 - **Benchmark Framework**: https://github.com/yangzhou12/BenchX
 
@@ -37,44 +37,46 @@ Please follow the [instruction](https://github.com/yangzhou12/BenchX/blob/releas
 
 ### 1. Classification
 
-To fine-tune M-FLAG for classification, run this command:
+To fine-tune REFERS for classification, run this command:
 
 ```
-python bin/train.py config/classification/<dataset_name>/M-FLAG.yml
+python bin/train.py config/classification/<dataset_name>/REFERS.yml
 ```
 
 ### 2. Segmentation
-To fine-tune M-FLAG for segmentation, run this command:
+To fine-tune REFERS for segmentation, run this command:
 
 ```
-python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/M-FLAG.yml
+python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/REFERS.yml
 ```
 
 ### 3. Report Generation
-To fine-tune M-FLAG for report generation, run this command:
+To fine-tune REFERS for report generation, run this command:
 ```
-python bin/train.py config/report_generation/<dataset_name>/M-FLAG.yml
+python bin/train.py config/report_generation/<dataset_name>/REFERS.yml
 ```
 
 ### 4. Evaluation
-To evaluate fine-tuned M-FLAG models, run:
+To evaluate fine-tuned REFERS models, run:
 
 ```
 # For classification and report generation
-python bin/test.py config/<task_name>/<dataset_name>/M-FLAG.yml validator.splits=[test] ckpt_dir=<path_to_checkpoint>
+python bin/test.py config/<task_name>/<dataset_name>/REFERS.yml validator.splits=[test] ckpt_dir=<path_to_checkpoint>
 
 # For segmentation
-python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/M-FLAG.yml <path_to_checkpoint>
+python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/REFERS.yml <path_to_checkpoint>
 ```
 
 ## Citations
 ```bibtex
-@inproceedings{huang2021M-FLAG,
-  title={M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization},
-  author={Liu, Che and Cheng, Sibo and Chen, Chen and Qiao, Mengyun and Zhang, Weitong and Shah, Anand and Bai, Wenjia and Arcucci, Rossella},
-  booktitle={Proceedings of MICCAI},
-  pages={637--647},
-  year={2023},
+@article{zhou2022generalized,
+  title={Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports},
+  author={Zhou, Hong-Yu and Chen, Xiaoyu and Zhang, Yinghao and Luo, Ruibang and Wang, Liansheng and Yu, Yizhou},
+  journal={Nature Machine Intelligence},
+  volume={4},
+  number={1},
+  pages={32--40},
+  year={2022}
 }
 ```
 ```bibtex