vit-multilabel-14

🫁 ViT-Base-Patch16-224 - Multi-Label Chest X-ray Classification (14 Pathologies)

Ce modèle a été entraîné pour la classification multi-label de 14 pathologies thoraciques à partir de radiographies X-ray du dataset ChestX-ray14.

📋 Description

• Architecture: ViT-Base-Patch16-224
• Tâche: Classification multi-label (14 pathologies)
• Dataset: NIH Chest X-ray (ChestX-ray14)
• Framework: PyTorch
• Image Size: 224×224

📊 Performance Globale

Comparaison avec l'article CheXNet

📈 Performance par Pathologie

🏷️ Les 14 Pathologies

⚙️ Configuration d'entraînement

{
  "data_variant": "full",
  "batch_size": 16,
  "image_size": 224,
  "num_classes": 14,
  "learning_rate": 0.0001,
  "num_epochs": 50,
  "scheduler": "ReduceLROnPlateau (factor=0.5, patience=5)",
  "optimizer": "AdamW (weight_decay=0.01)",
  "loss": "BCEWithLogitsLoss (non pond\u00e9r\u00e9e)"
}

🚀 Utilisation

import torch
from torchvision import transforms
from PIL import Image

# Charger le modèle
# Pour ViT
import timm

model = timm.create_model(
    'vit_base_patch16_224',
    pretrained=False,
    num_classes=14
)
model.load_state_dict(torch.load('pytorch_model.bin', map_location='cpu'))
model.eval()

# Préprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Pathologies
PATHOLOGIES = [
    "Atelectasis", "Cardiomegaly", "Effusion", "Infiltration",
    "Mass", "Nodule", "Pneumonia", "Pneumothorax", "Consolidation",
    "Edema", "Emphysema", "Fibrosis", "Pleural_Thickening", "Hernia"
]

# Prédiction
image = Image.open('chest_xray.png').convert('RGB')
input_tensor = transform(image).unsqueeze(0)

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

# Afficher les probabilités
for name, prob in zip(PATHOLOGIES, probs[0]):
    print(f"{name}: {prob:.4f}")

📚 Citation

@inproceedings{Wang_2017,
    title = {ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks},
    author = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
    booktitle = {IEEE CVPR},
    year = {2017}
}

@article{rajpurkar2017chexnet,
    title={CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning},
    author={Rajpurkar, Pranav and others},
    journal={arXiv preprint arXiv:1711.05225},
    year={2017}
}

📄 License

MIT License