Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+library_name: pytorch
+tags:
+- Medical Vsion-Language Pre-Training
+- BenchX
+---
+# MedKLIP Checkpoint Model Card
+
+A retrained MedKLIP model for benchmarking medical vision-language pre-training methods within the BenchX framework.
+
+## Model Details
+- **Model Type**: MedKLIP
+- **Architecture**: ResNet-50 image encoder and custom BERT text encoder
+- **Original Papers**: [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://arxiv.org/abs/2301.02228)
+- **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 MedKLIP for classification, run this command:
+
+```
+python bin/train.py config/classification/<dataset_name>/MedKLIP.yml
+```
+
+### 2. Segmentation
+To fine-tune MedKLIP for segmentation, run this command:
+
+```
+python mmsegmentation/tools/train.py config/benchmark/<dataset_name>/MedKLIP.yml
+```
+
+### 3. Report Generation
+To fine-tune MedKLIP for report generation, run this command:
+```
+python bin/train.py config/report_generation/<dataset_name>/MedKLIP.yml
+```
+
+### 4. Evaluation
+To evaluate fine-tuned MedKLIP models, run:
+
+```
+# For classification and report generation
+python bin/test.py config/<task_name>/<dataset_name>/MedKLIP.yml validator.splits=[test] ckpt_dir=<path_to_checkpoint>
+
+# For segmentation
+python mmsegmentation/tools/my_test.py mmsegmentation/config/<dataset_name>/MedKLIP.yml <path_to_checkpoint>
+```
+
+## Citations
+```bibtex
+@inproceedings{wu2023medklip,
+  title={{MedKLIP}: Medical Knowledge Enhanced Language-Image Pre-Training},
+  author={Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
+  journal={Proceedings of ICCV},
+  pages = "21372--21383",
+  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}
+}
+```