RadioDino-s16

🧠 Developed by Luca Zedda, Andrea Loddo, and Cecilia Di Ruberto 🏥 Department of Mathematics and Computer Science, University of Cagliari 📄 Published in: Computers in Biology and Medicine, 2025

Model Details

• Architecture: ViT-small with patch size 16 (s16)
• SSL framework: DINO (self-distillation without labels)
• Pretraining dataset: RadImageNet (1.35M CT/MRI/Ultrasound images)
• Embedding size: 384
• Applications: Feature extraction, classification backbones, transfer learning, medical imaging analysis

Example Usage

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

# Load model from Hugging Face Hub
model = timm.create_model("hf_hub:Snarcy/RadioDino-s16", pretrained=True)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Load and preprocess a sample image
image = Image.open("path/to/your/image").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225]),
])
input_tensor = transform(image).unsqueeze(0).to(device)

# Forward pass to obtain feature embedding
with torch.no_grad():
    embedding = model(input_tensor)

📝 Citation

If you use this model, please cite the following paper:

Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis Luca Zedda, Andrea Loddo, Cecilia Di Ruberto Computers in Biology and Medicine, Volume 195, 2025, 110583 https://doi.org/10.1016/j.compbiomed.2025.110583

@article{ZEDDA2025110583,
  title = {Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis},
  journal = {Computers in Biology and Medicine},
  volume = {195},
  pages = {110583},
  year = {2025},
  issn = {0010-4825},
  doi = {https://doi.org/10.1016/j.compbiomed.2025.110583},
  url = {https://www.sciencedirect.com/science/article/pii/S0010482525009345},
  author = {Luca Zedda and Andrea Loddo and Cecilia {Di Ruberto}},
  keywords = {Radiomics, Self-supervised learning, Deep learning, DINO, DINOV2, Medical imaging, Feature extraction, Generalizability},
}