Update README.md

README.md

@@ -1,3 +1,68 @@
----
-license: mit
----
+---
+license: mit
+---
+# SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
+
+**SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation** [[Paper](https://arxiv.org/abs/2409.13321)] <br>
+
+## Contents
+- [Install](#install)
+- [LLaVA-Phi Weights](#llava-weights)
+- [Train](#train)
+- [Evaluation](#evaluation)
+
+## Environment
+
+```Shell
+conda create -n slava_cxr python=3.10 -y
+conda activate slava_cxr
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+```
+
+## MODEL
+The SLaVA-CXR model can be downloaded in [HuggingFace](https://drive.google.com/file/d/1c0BXEuDy8Cmm2jfN0YYGkQxFZd2ZIoLg/view). 
+
+## Train
+The training codes is made available. The training datasets are currently not available.
+
+## Evaluation
+Evaluation dataset can be any chest X-ray frontal view image paired with a report.  
+We used MIMIC-CXR and IU-Xray datasets in our paper for the evaluation.
+We have included IU-Xray questions for impression and findings section automation.
+Please download IU-Xray dataset [LINK](https://drive.google.com/file/d/1c0BXEuDy8Cmm2jfN0YYGkQxFZd2ZIoLg/view). 
+
+### Findings Generation
+```Shell
+CUDA_VISIBLE_DEVICES=0 python -m llava_phi.eval.model_vqa_slava_cxr \
+    --model-path ./SLaVA-CXR \
+    --question-file iuxray_sample_findings.jsonl \
+    --image-folder path_to_iuxray_images \
+    --answers-file findings_result.jsonl \
+    --conv-mode default \
+    --max_new_tokens 512
+```
+### Impression Summarization
+```Shell
+CUDA_VISIBLE_DEVICES=0 python -m llava_phi.eval.model_vqa_slava_cxr \
+    --model-path ./SLaVA-CXR \
+    --question-file iuxray_sample_impression.jsonl \
+    --image-folder path_to_iuxray_images \
+    --answers-file impression_result.jsonl \
+    --conv-mode default \
+    --max_new_tokens 256
+```
+## Citation
+```bibtex
+
+@article{wu2024slava,
+  title={SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation},
+  author={Wu, Jinge and Kim, Yunsoo and Shi, Daqian and Cliffton, David and Liu, Fenglin and Wu, Honghan},
+  journal={arXiv preprint arXiv:2409.13321},
+  year={2024}
+}
+```
+
+## Acknowledgement
+We used the LLaVA-Phi codes to train our model
+- [LLaVA-Phi](https://github.com/zhuyiche/llava-phi)