---
language: en
license: apache-2.0
tags:
- medical
- computer-vision
- image-classification
- multi-label-classification
- chest-xray
- radiology
- pytorch
- convnext
- cbam
- attention
- chexpert
datasets:
- stanfordmlgroup/chexpert
metrics:
- auc
library_name: pytorch
---

# ConvNeXt-Base CheXpert Classifier with CBAM Attention

[![GitHub](https://img.shields.io/badge/GitHub-Code%20%26%20Training-black?logo=github)](https://github.com/jikaan/convnext-chexpert-attention)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/jikaan/convnext-chexpert-attention/blob/main/LICENSE)
[![Dataset: CheXpert](https://img.shields.io/badge/Dataset-CheXpert-green)](https://stanfordmlgroup.github.io/competitions/chexpert/)

Fine-tuned **ConvNeXt-Base** with **CBAM attention** for multi-label classification of 14 thoracic pathologies from chest X-rays. Iteration 6 (final model) with **0.81 AUC**.

📚 **Full training code, examples & scripts**: [GitHub Repository](https://github.com/jikaan/convnext-chexpert-attention)

---

## 🔬 Model Overview

This is a production-ready classifier for automated chest X-ray interpretation. The model combines modern ConvNeXt architecture with Convolutional Block Attention Module (CBAM) for improved pathology detection and localization.

**Key Specs:**
- Architecture: ConvNeXt-Base + CBAM
- Training Iteration: 6 (final)
- Validation AUC: **0.81**
- Input: 384×384 frontal chest X-rays
- Output: 14 pathology probabilities
- Model Size: 300MB
- Parameters: ~88M + CBAM

---

## 📋 Detectable Pathologies (14 Classes)

| # | 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 |

---

## 📊 Performance Results

**Iteration 6 (Final Model)**
- **Overall Validation AUC: 0.81**
- Training approach: Multi-iteration refinement with CBAM attention
- Dataset: CheXpert (Stanford ML Group, 224K+ images)

**Model outputs:** Sigmoid-activated probabilities for each pathology (0-1 range)

---

## 🖼️ GradCAM Visualizations

Model predictions with attention maps showing pathology localization:

### Example 1: Edema Detection
- **Prediction:** Edema 63.7%
- **Visualization:** GradCAM highlights fluid accumulation regions

![Edema GradCAM](analysis.png)

### Example 2: Fracture Detection
- **Prediction:** Fracture 67.2%
- **Visualization:** GradCAM highlights rib/bone fracture area

![Fracture GradCAM](1.png)

### Example 3: Pleural Other
- **Prediction:** Pleural Other 65.7%
- **Visualization:** GradCAM shows pleural involvement

![Pleural Other GradCAM](2.png)

### Example 4: Atelectasis Detection
- **Prediction:** Atelectasis 63.1%
- **Visualization:** GradCAM localizes collapsed lung regions

![Atelectasis GradCAM](3.png)

---

## 🚀 Quick Start

### Installation

```bash
pip install torch torchvision timm Pillow opencv-python
```

### Basic Inference

```python
import torch
from PIL import Image
from torchvision import transforms
import timm

# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = timm.create_model('convnext_base', pretrained=False, num_classes=14)
model.load_state_dict(torch.load('model.pth', map_location=device))
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Grayscale(num_output_channels=3),
    transforms.Resize((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.5029414296150208]*3,
        std=[0.2892409563064575]*3
    )
])

# Predict
image = Image.open('chest_xray.jpg')
input_tensor = transform(image).unsqueeze(0).to(device)

with torch.no_grad():
    logits = model(input_tensor)
    probs = torch.sigmoid(logits)

pathologies = [
    "No Finding", "Enlarged Cardiomediastinum", "Cardiomegaly",
    "Lung Opacity", "Lung Lesion", "Edema", "Consolidation",
    "Pneumonia", "Atelectasis", "Pneumothorax", "Pleural Effusion",
    "Pleural Other", "Fracture", "Support Devices"
]

for pathology, prob in zip(pathologies, probs[0]):
    print(f"{pathology}: {prob.item():.3f}")
```

---

## 🏗️ Model Architecture

**ConvNeXt-Base:**
- Modern efficient architecture (Liu et al., 2022)
- ImageNet-22k pretrained weights
- Inverted bottleneck design
- LayerNorm + GELU activations

**CBAM Attention Module:**
- Channel attention: Refines feature importance
- Spatial attention: Highlights important regions
- Lightweight addition to base architecture
- Improves pathology localization

**Result:** Better accuracy + interpretability with GradCAM

---

## 📚 Dataset Information

- **Source:** [CheXpert Dataset](https://stanfordmlgroup.github.io/competitions/chexpert/) (Stanford ML Group)
- **Size:** 224,316 chest X-rays from 65,240 patients
- **Period:** October 2002 - July 2017 (Stanford Hospital)
- **Labels:** 14 pathologies auto-extracted from radiology reports
- **Uncertainty:** Labels include uncertainty handling (-1 for uncertain)

---

## ⚠️ 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 & Attribution

**You MUST cite this work if used in publications:**

```bibtex
@software{convnext_chexpert_attention_2025,
  author = {Time},
  title = {ConvNeXt-Base CheXpert Classifier with CBAM Attention},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/calender/Convnext-Chexpert-Attention}
}

@article{irvin2019chexpert,
  title={CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and others},
  journal={AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={590--597},
  year={2019}
}
```

**Claiming you trained this model when you didn't is scientific misconduct.**

---

## 🔗 Links

- **GitHub (Code + Training):** https://github.com/jikaan/convnext-chexpert-attention
- **CheXpert Dataset:** https://stanfordmlgroup.github.io/competitions/chexpert/
- **Paper:** 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**