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anassaifi8912/chestxray-blip-report-generator

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🩻 ChestXray-BLIP Report Generator

AI-powered Chest X-ray → Radiology Report Generation

🚀 Overview

ChestXray-BLIP Report Generator is a Vision–Language deep learning model fine-tuned to automatically generate radiology-style chest X-ray reports from medical images.

The model is trained on the large-scale NIH ChestX-ray14 dataset (~45GB) and designed for medical AI research, education, and experimentation. It is also integrated into an end-to-end medical application with an AI chatbot interface.

⚠️ Disclaimer: This model is not clinically approved and must not be used for real-world diagnosis.

🌟 Key Features

—🩺 Automated chest X-ray report generation
—🧠 Vision–Language architecture (BLIP-based)
—🖼️ Image → Text medical understanding

—💬 Compatible with AI medical chatbots (Qwen-based systems)
—⚡ FP16 / mixed-precision support
—Trained on kagggle 2 T4 GPUs

🧠 Model Details

Attribute	Description
Task	Chest X-ray → Radiology Report Generation
Model Type	Vision-Language Model
Base Architecture	BLIP (Bootstrapped Language-Image Pretraining)
Framework	PyTorch + Hugging Face Transformers
Vision Encoder	ViT (BLIP)
Text Decoder	Transformer-based
Training Dataset	NIH ChestX-ray14 (~45GB)
Training Epochs	3
Precision	FP16 supported
Language	English

🧬 Architecture Overview

Chest X-ray Image ↓ Vision Encoder (ViT / BLIP) ↓ Cross-Modal Attention ↓ Text Decoder ↓ Radiology-Style Report

📦 Model Files

bestmodel/ ├── config.json ├── model.safetensors ├── generationconfig.json ├── preprocessorconfig.json ├── tokenizer.json ├── tokenizerconfig.json ├── vocab.txt └── specialtokensmap.json

📥 Usage

🔧 Load the Model

python

from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained(
    "anassaifi8912/chestxray-blip-report-generator"
)

model = BlipForConditionalGeneration.from_pretrained(
    "anassaifi8912/chestxray-blip-report-generator"
)

🖼️ Generate a Report
inputs = processor(image, return_tensors="pt")
outputs = model.generate(**inputs, max_length=128)

report = processor.decode(outputs[0], skip_special_tokens=True)
print(report)

📊 Evaluation Metrics

Evaluated on unseen test data only.

Metric	Score
BLEU-1	0.1019
BLEU-2	0.0692
BLEU-3	0.0341
BLEU-4	0.0189
METEOR	0.1692
ROUGE-L	0.1803
🏥 Clinical Accuracy	0.3159

🧪 Intended Use

✅ Medical AI research
✅ Academic & educational projects
✅ Prototyping healthcare applications

❌ Not for clinical diagnosis or treatment

⚠️ Limitations

Generated reports may be synthetic or incomplete

No disease localization or bounding boxes

Single-image input only

Performance varies across disease categories

Requires expert human verification

🔐 Ethical Considerations

Outputs are AI-generated

May contain hallucinations or inaccuracies

Trained on publicly available medical data

Not evaluated for demographic or clinical bias

🏥 Medical Disclaimer

This model is not a medical device and is not FDA-approved.
It must not be used for real-world clinical diagnosis, treatment, or medical decision-making.

📚 Dataset

NIH ChestX-ray14 Dataset

Publicly available via NIH & Kaggle

Over 112,000 chest X-ray images

14 disease labels

👤 Author

Anas Saifi

AI / Data Scientist

🔗 GitHub: https://github.com/anassaifi775

🔗 Hugging Face: https://huggingface.co/anassaifi8912

🔗 LinkedIn : https://www.linkedin.com/in/mohd-anas-6570a6290/

⭐ Acknowledgements

NIH Clinical Center

Kaggle

Hugging Face 🤗

PyTorch Community

⭐ If you find this model useful, please give it a star on Hugging Face