calender/Convnext-Chexpert-Attention

ConvNeXt-CheXpert: CBAM-Augmented Thoracic Classifier

Model Description

This repository contains the weights for a ConvNeXt-Base architecture fine-tuned for multi-label classification of chest radiographs. The model is augmented with Convolutional Block Attention Modules (CBAM) to enhance feature localization and interpretability.

Try the live demo here: 👉 GRADCAM-Convnext-Chexpert-Attention Space

• Architecture: ConvNeXt-Base + CBAM
• Validation AUC: 0.81 (Iteration 6)
• Input Resolution: 384x384

Detectable Classes

The model outputs probabilities for the following 14 classes:

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

Interpretability (Grad-CAM)

The model's attention mechanism allows for precise localization of pathologies.

Figure 1: Multi-label detection visualized via Grad-CAM.

Usage

Prerequisites

pip install torch torchvision timm pillow

Inference Code

This script loads the model and performs inference using the specific normalization statistics from training. Python

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

# 1. Configuration
# Ensure you have downloaded model.pth from this repository
MODEL_PATH = "model.pth" 
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

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

# 2. Load Model
model = timm.create_model('convnext_base', pretrained=False, num_classes=14)
model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
model.to(DEVICE)
model.eval()

# 3. Preprocessing
# Note: These specific mean/std values are critical for accuracy
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
    )
])

# 4. Run Prediction
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)[0]

print("Predictions:")
for name, score in zip(pathologies, probs):
    print(f"{name}: {score.item():.3f}")

Citation

@misc{convnext_chexpert_2025,
  author = {Calendar, S.},
  title = {ConvNeXt-CheXpert: CBAM-Augmented Thoracic Classifier},
  year = {2025},
  publisher = {Hugging Face},
  url = {[https://huggingface.co/calender/GRADCAM-Convnext-Chexpert-Attention](https://huggingface.co/calender/GRADCAM-Convnext-Chexpert-Attention)}
}```