Update README.md

README.md

@@ -82,43 +82,23 @@ This model is intended for research purposes in the field of neuropathology.
 * **Results:** 
   The model achieved strong performance across multiple evaluation methods using the Neuro Path dataset.
 
-  **Linear Probe Performance:**
-  - Accuracy: 84.51%
-  - Precision: 83.83%
-  - Recall: 84.51%
-  - F1 Score: 83.68%
-
-  **K-Nearest Neighbors Classification:**
-  - Accuracy: 87.20%
-  - Precision: 86.91%
-  - Recall: 87.20%
-  - F1 Score: 86.76%
-
 ### Model Comparison
-
-#### Models Evaluated
+ 
 * **MAD-NP:** Our model 
-* **dinov2-with-registers-giant:** [facebook/dinov2-with-registers-giant](https://huggingface.co/facebook/dinov2-with-registers-giant)
-* **dinov3-base:** [facebook/dinov3-vitb16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m) 
-* **dinov3-7b:** [facebook/dinov3-vit7b16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vit7b16-pretrain-lvd1689m)
-* **dinov3-small:** [facebook/dinov3-vits16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m)
-* **dinov3-small-plus:** [facebook/dinov3-vits16plus-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vith16plus-pretrain-lvd1689m)
-* **dinov3-huge:** [facebook/dinov3-vith16plus-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vith16plus-pretrain-lvd1689m)
-* **dinov3-large:** [facebook/dinov3-vitl16-pretrain-sat493m](https://huggingface.co/facebook/dinov3-vitl16-pretrain-sat493m)
+* **dinov2-with-registers-giant:** [facebook/dinov2-with-registers-giant](https://huggingface.co/facebook/dinov2-with-registers-giant) 
 * **uni:** [MahmoodLab/UNI](https://huggingface.co/MahmoodLab/UNI)
 * **uni2:** [MahmoodLab/UNI2](https://huggingface.co/MahmoodLab/UNI2)
 * **prov-gigapath:** [prov-gigapath/prov-gigapath](https://huggingface.co/prov-gigapath/prov-gigapath)
-* 
-### Unified Model Performance Comparison (Representation & Clustering Only)
-
-| Model | Linear F1 ↑ | k-NN F1 ↑ | AMI ↑ | DBI ↓ | Silhouette ↑ |
-|-------|-------------|-----------|--------|--------|----------------|
-| **MAD-NP** | **0.9307** | **0.9286** | **0.7668** | **1.2821** | N/A |
-| UNI2 | 0.9252 | 0.9209 | 0.4478 | 2.3078 | N/A |
-| Prov-GigaPath | 0.9273 | 0.9215 | 0.4732 | 2.0342 | N/A |
+* **Virchow2:** [paige-ai/Virchow2](https://huggingface.co/paige-ai/Virchow2)
+
+| Model | Linear F1 ↑ | k-NN F1 ↑ | AMI ↑ | DBI ↓ | 
+|-------|-------------|-----------|--------|--------|
+| **MAD-NP** | **0.9307** | **0.9286** | **0.7668** | **1.2821** |
+| UNI2 | 0.9252 | 0.9209 | 0.4478 | 2.3078 |
+| Prov-GigaPath | 0.9273 | 0.9215 | 0.4732 | 2.0342 |
 | UNI | 0.9245 | 0.9284 | 0.2975 | 1.9559 | N/A |
-| DINOv2-Giant (FT) | 0.9146 | 0.9092 | 0.5275 | 1.4133 | N/A |
-| Virchow2 | 0.9135 | 0.9072 | 0.3597 | 1.2867 | N/A |
+| DINOv2-Giant (FT) | 0.9146 | 0.9092 | 0.5275 | 1.4133 |
+| Virchow2 | 0.9135 | 0.9072 | 0.3597 | 1.2867 |