---
title: ConvNeXt CheXpert Classifier with GradCAM
emoji: 🫁
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "4.0.0"
app_file: app.py
pinned: false
license: apache-2.0
---

# 🫁 ConvNeXt CheXpert Classifier with GradCAM

A web-based chest X-ray analysis tool using ConvNeXt-Base with CBAM attention mechanism. This app provides multi-label classification of 14 thoracic pathologies with GradCAM visualization showing where the model focuses its attention.

## ✨ Features

- 🔍 **Multi-label Classification**: Detects 14 different chest conditions
- 📊 **Confidence Filtering**: Only shows predictions above 60% confidence
- 🎯 **GradCAM Visualization**: See exactly where the model is looking
- 🖼️ **Interactive Interface**: Easy-to-use web interface via Gradio
- 🏥 **Research Ready**: Optimized for medical imaging research

## 📋 Supported Conditions

| # | Pathology | # | Pathology |
|---|---|---|---|
| 1 | No Finding | 8 | Pneumonia |
| 2 | Enlarged Cardiomediastinum | 9 | Atelectasis |
| 3 | Cardiomegaly | 10 | Pneumothorax |
| 4 | Lung Opacity | 11 | Pleural Effusion |
| 5 | Lung Lesion | 12 | Pleural Other |
| 6 | Edema | 13 | Fracture |
| 7 | Consolidation | 14 | Support Devices |

## 🚀 Quick Start

1. **Upload**: Click "Upload Chest X-ray" and select a chest X-ray image
2. **Analyze**: The model will process the image and show confident predictions
3. **Review**: View GradCAM visualizations showing model attention regions

## 📊 How It Works

### Model Architecture
- **Backbone**: ConvNeXt-Base (modern efficient architecture)
- **Attention**: CBAM (Convolutional Block Attention Module)
- **Input**: 384×384 chest X-rays (automatically resized)
- **Output**: 14 pathology probabilities with sigmoid activation

### GradCAM Visualization
- **Heatmap**: Shows attention intensity (red = high attention)
- **Overlay**: Superimposes attention map on original X-ray
- **Confidence**: Only displays findings above 60% confidence threshold

## 🏗️ Technical Details

### Model Performance
- **Validation AUC**: 0.81 (multi-label)
- **Parameters**: ~88M + CBAM attention
- **Training Data**: CheXpert dataset (224K+ chest X-rays)
- **Framework**: PyTorch + timm library

### Dependencies
```bash
pip install -r requirements.txt
```

## ⚠️ Important Medical Disclaimer

**🚨 FOR RESEARCH & EDUCATION ONLY 🚨**

### ❌ DO NOT USE FOR:
- Clinical diagnosis or treatment decisions
- Emergency medical situations
- Replacing professional radiologist review
- Patient care without expert validation

### ⚠️ Limitations:
- Not clinically validated or FDA-approved
- Trained on historical Stanford data (2002-2017)
- Performance may vary on different populations/equipment
- Requires qualified radiologist review for any clinical use

### ✅ Appropriate Uses:
- Academic research and benchmarking
- Algorithm development and comparison
- Educational demonstrations
- Proof-of-concept prototypes

**Always consult qualified healthcare professionals for medical decisions.**

## 📚 Citation

If you use this work in publications, please cite:

```bibtex
@software{convnext_chexpert_attention_2025,
  author = {Time},
  title = {ConvNeXt-Base CheXpert Classifier with CBAM Attention},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/spaces/your-username/convnext-chexpert-gradcam}
}
```

## 🔗 Links

- **Original Repository**: [GitHub](https://github.com/jikaan/convnext-chexpert-attention)
- **CheXpert Dataset**: [Stanford ML Group](https://stanfordmlgroup.github.io/competitions/chexpert/)
- **Paper**: [CheXpert: A large chest radiograph dataset](https://arxiv.org/abs/1901.07031)

## 📄 License

Apache License 2.0 - See [LICENSE](https://github.com/jikaan/convnext-chexpert-attention/blob/main/LICENSE) for details.

---

**Created by Time | October 2025**