Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: pytorch
+tags:
+- Medical Vsion-Language Pre-Training
+- BenchX
+---
+
+# M-FLAG Checkpoint Model Card
+
+A retrained M-FLAG 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)
+- **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
+
+## Intended Use
+- **Primary Use Cases**:
+  - Benchmarking performance for Medical Image Classification
+  - Benchmarking performance for Medical Image Segmentation
+  - Benchmarking performance for Medical Report Generation
+
+## Pre-Training Data
+- **Dataset**: 
+  - Data source(s): MIMIC-CXR
+  - Types of medical images: Frontal chest X-rays
+  - Text data type: Associated radiology reports
+
+## Prerequisites
+
+Please follow the [instruction](https://github.com/yangzhou12/BenchX/blob/release/README.md#installation) to install BenchX. 
+
+## Training & Evaluation
+
+### 1. Classification
+
+To fine-tune M-FLAG for classification, run this command:
+
+```
+python bin/train.py config/classification/<dataset_name>/M-FLAG.yml
+```
+
+### 2. Segmentation
+To fine-tune M-FLAG for segmentation, run this command:
+
+```
+python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/M-FLAG.yml
+```
+
+### 3. Report Generation
+To fine-tune M-FLAG for report generation, run this command:
+```
+python bin/train.py config/report_generation/<dataset_name>/M-FLAG.yml
+```
+
+### 4. Evaluation
+To evaluate fine-tuned M-FLAG 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>
+
+# For segmentation
+python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/M-FLAG.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},
+}
+```
+```bibtex
+@inproceedings{zhou2024benchx,
+  title={BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays},
+  author={Yang Zhou, Tan Li Hui Faith, Yanyu Xu, Sicong Leng, Xinxing Xu, Yong Liu, Rick Siow Mong Goh},
+  booktitle={Proceedings of NeurIPS},
+  year={2024}
+}
+```