Update model card with OmniRad paper and code links

README.md

@@ -1,81 +1,86 @@
 ---
+datasets:
+- medmnist
+- radimagenet
+- BUSI
+library_name: timm
 license: cc-by-4.0
+pipeline_tag: image-feature-extraction
 tags:
-  - radiomics
-  - medical-imaging
-  - vision-transformer
-  - dino
-  - dinov2
-  - feature-extraction
-  - foundation-model
-library_name: timm
-datasets:
-  - medmnist
-  - radimagenet
-  - BUSI
-pipeline_tag: feature-extraction
+- radiomics
+- medical-imaging
+- vision-transformer
+- dino
+- dinov2
+- feature-extraction
+- foundation-model
 model-index:
-  - name: RadioDINO-b16
-    results:
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: BreastMNIST
-          type: BreastMNIST
-        metrics:
-          - type: F1
-            value: 87.69
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: PneumoniaMNIST
-          type: PneumoniaMNIST
-        metrics:
-          - type: F1
-            value: 93.29
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: OrganAMNIST
-          type: OrganAMNIST
-        metrics:
-          - type: F1
-            value: 97.20
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: OrganCMNIST
-          type: OrganCMNIST
-        metrics:
-          - type: F1
-            value: 94.57
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: OrganSMNIST
-          type: OrganSMNIST
-        metrics:
-          - type: F1
-            value: 78.15
-      - task:
-          type: image-classification
-          name: Image Classification
-        dataset:
-          name: BUSI
-          type: BUSI
-        metrics:
-          - type: F1
-            value: 91.73
+- name: RadioDINO-b16
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: BreastMNIST
+      type: BreastMNIST
+    metrics:
+    - type: F1
+      value: 87.69
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: PneumoniaMNIST
+      type: PneumoniaMNIST
+    metrics:
+    - type: F1
+      value: 93.29
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: OrganAMNIST
+      type: OrganAMNIST
+    metrics:
+    - type: F12
+      value: 97.2
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: OrganCMNIST
+      type: OrganCMNIST
+    metrics:
+    - type: F1
+      value: 94.57
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: OrganSMNIST
+      type: OrganSMNIST
+    metrics:
+    - type: F1
+      value: 78.15
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: BUSI
+      type: BUSI
+    metrics:
+    - type: F1
+      value: 91.73
 ---
 
 # RadioDINO-b16
 
-**RadioDINO-b16** 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.
+**RadioDINO-b16** is a self-supervised Vision Transformer foundation model developed for radiomics and medical imaging. This model is part of the **OmniRad** framework.
+
+- **Paper:** [OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis](https://huggingface.co/papers/2602.04547)
+- **Repository:** [OmniRad GitHub](https://github.com/unica-visual-intelligence-lab/OmniRad)
+
+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-b16 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.
 
@@ -126,7 +131,11 @@ with torch.no_grad():
 
 ## 📝 Citation
 
-If you use this model, please cite the following paper:
+If you use this model, please cite the following papers:
+
+**OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis**  
+Luca Zedda, Andrea Loddo, Cecilia Di Ruberto  
+[https://huggingface.co/papers/2602.04547](https://huggingface.co/papers/2602.04547)
 
 **Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis**  
 Luca Zedda, Andrea Loddo, Cecilia Di Ruberto