Update README.md

README.md

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----
-tags:
-- image-classification
-- timm
-library_name: timm
-license: apache-2.0
----
-# Model card for RadioDino-s16
+---
+license: cc-by-4.0
+tags:
+  - radiomics
+  - medical-imaging
+  - vision-transformer
+  - dino
+  - dinov2
+  - feature-extraction
+  - foundation-model
+library_name: timm
+datasets:
+  - medmnist
+  - radimagenet
+  - BUSI
+pipeline_tag: feature-extraction
+model-index:
+  - name: RadioDINO-s16
+    results:
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: MedMNISTv2
+          type: BreastMNIST
+        metrics:
+          - type: F1
+            value: 88.98
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: MedMNISTv2
+          type: PneumoniaMNIST
+        metrics:
+          - type: F1
+            value: 90.86
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: MedMNISTv2
+          type: OrganAMNIST
+        metrics:
+          - type: F1
+            value: 96.47 
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: MedMNISTv2
+          type: OrganCMNIST
+        metrics:
+          - type: F1
+            value: 93.63
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: MedMNISTv2
+          type: OrganSMNIST
+        metrics:
+          - type: F1
+            value: 77.73 
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: BUSI
+          type: BUSI
+        metrics:
+          - type: F1
+            value: 81.83
+
+---
+
+# RadioDINO-s16
+
+**RadioDINO-s16** is a self-supervised Vision Transformer foundation model developed for radiomics and medical imaging. It is based on the DINO framework and pretrained on the large-scale **RadImageNet** dataset (1.35 million CT, MRI, and Ultrasound images across 165 classes and 11 anatomical regions). This model is part of the *Radio DINO* family and was created to extract robust, general-purpose features for downstream medical tasks including classification, segmentation, and interpretability analysis.
+
+Unlike traditional radiomics methods that rely on handcrafted features and supervised models pretrained on natural images, RadioDINO-s16 offers a domain-adapted alternative that consistently outperforms previous models on diverse medical benchmarks. It has been rigorously validated on the MedMNISTv2 benchmark suite and shown to be effective even without fine-tuning.
+
+> 🧠 Developed by [Luca Zedda](https://orcid.org/0009-0001-8488-1612), [Andrea Loddo](https://orcid.org/0000-0002-6571-3816), and [Cecilia Di Ruberto](https://orcid.org/0000-0003-4641-0307)  
+> 🏥 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
+
+```python
+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](https://doi.org/10.1016/j.compbiomed.2025.110583)
+
+```bibtex
+@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},
+}
+```