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CKJ26/Dual-View-Slava-Final
Dual-View-Slava-Final is a machine learning model from CKJ26. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Dual-View SLaVA-CXR is a vision-language model for structured radiology report generation from frontal and lateral chest X-rays. Built on the Re³ (Recognize–Reason–Report) paradigm and extending the original SLaVA-CXR…
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From the Hugging Face model README
Dual-View SLaVA-CXR is a vision-language model for structured radiology report generation from frontal and lateral chest X-rays. Built on the Re³ (Recognize–Reason–Report) paradigm and extending the original SLaVA-CXR model, this project integrates dual-view vision fusion and leverages CLIP, BiomedCLIP, and Phi-2 for enhanced anatomical reasoning.
├── Data Collection and Preprocessing/
│ ├── Data_collection_Mimic.ipynb
│ ├── Data_preprocess.ipynb
│ ├── Radgraph Based Report Cleaning.ipynb
│ └── train_data_json_gen.ipynb
│
├── Evaluate/
│ ├── Evaluate.ipynb
│ └── Results_IU_Xray/ # Contains evaluation results on IU X-ray dataset
│
├── llava_phi/
│ ├── Dual Slava train.ipynb # Training pipeline
│ └── generation.ipynb # Inference/report generation
│
├── requirements.txt
└── README.md
Dual-Encoder Fusion: Combines CLIP and BiomedCLIP for each view with learnable weight α:
Cross-View Attention: Enables anatomical reasoning across views:
Gated Feature Fusion:
Re³ Pipeline:
| Dataset | BLEU | ROUGE-L | METEOR | BERT | RadGraph F1 | CheXbert F1 |
|---|---|---|---|---|---|---|
| MIMIC-CXR | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| IU X-Ray | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
(Results in /Evaluate/Results_IU_Xray)
# Clone repo
git clone https://github.com/Clintonkjkj/Dual-View-Slava-CXR.git
cd Dual-View-Slava-CXR
# Set up virtual environment
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
Huggingface - https://huggingface.co/CKJ26/Dual-View-Slava-Final
Use llava_phi/Dual Slava train.ipynb after preparing data using:
Data_collection_Mimic.ipynbData_preprocess.ipynbRadgraph Based Report Cleaning.ipynbtrain_data_json_gen.ipynbUse llava_phi/generation.ipynb with both frontal and lateral views, plus a prompt (e.g., "Generate a radiology report").

@misc{dualviewslava2025,
title={Dual View SLaVA-CXR: Structured Radiology Reporting via Multi-View Chest X-rays},
author={Clinton KJ et al.},
year={2025},
note={Capstone Project}
}
This repository is provided for academic research purposes only.