Update README.md

README.md

@@ -53,7 +53,7 @@ This model is a Vision Transformer adapted for neuropathology tasks, developed u
 
 * **Model Type:** Vision Transformer (ViT) for neuropathology.
 * **Developed by:** Center for Applied Artificial Intelligence (CAAI)
-* **Model Date:** 05/05/2025
+* **Model Date:** 05/2025
 * **Base Model Architecture:** Dinov2-giant (https://huggingface.co/facebook/dinov2-giant)
 * **Input:** Image (224x224).
 * **Output:** Class token and patch tokens. These can be used for various downstream tasks (e.g., classification, segmentation, similarity search).
@@ -62,14 +62,7 @@ This model is a Vision Transformer adapted for neuropathology tasks, developed u
 * **Image Size Compatibility:**
     * The model was trained on images/patches of size 224x224.
     * The model can accept images of any size, not just the 224x224 dimensions used in training.
-* **License:** [PLACEHOLDER: Reiterate license chosen in YAML, e.g., Apache 2.0. Add link to full license if custom or 'other'.]
-* **Repository:** [PLACEHOLDER: Link to your model repository (e.g., GitHub, Hugging Face Hub)]
-* **Paper(s)/Reference(s):**
-    * [PLACEHOLDER: Link to your paper if applicable]
-    * [Optional: Link to relevant University of Kentucky data descriptor or study paper]
-    * Oquab et al., "DINOv2: Learning Robust Visual Features without Supervision" (https://arxiv.org/abs/2304.07193)
-    * Darcet et al., "Vision Transformers Need Registers" (https://arxiv.org/abs/2309.16588) (if registers are used)
-* **Demo:** [PLACEHOLDER: Link to your demo, if any]
+* **License:** Apache 2.0
 
 ## Intended Uses
 
@@ -80,9 +73,8 @@ This model is intended for research purposes in the field of neuropathology.
     * Feature extraction for quantitative analysis of neuropathology.  
 ## How to Get Started with the Model
 
-[PLACEHOLDER: Provide code snippets for loading and using your model. If available on Hugging Face, show an example using `transformers` or `torch.hub.load`.]
 
-Example using Hugging Face `transformers` (adjust based on your actual model and task):
+Three example methods using Hugging Face `transformers` (adjust based on your actual model and task):
 ```python
 
 import torch
@@ -214,8 +206,8 @@ if __name__ == "__main__":
 ## Training Data
 
 * **Dataset(s):** The model was trained on data from the University of Kentucky.
-    * **Name/Identifier:** [PLACEHOLDER: Specify the formal name or internal identifier of the dataset, e.g., "UKy Alzheimer's Disease Center Neuropathology Whole Slide Image Cohort v1.0"]
-    * **Source:** University of Kentucky, [PLACEHOLDER: Specific Department, Center, or PI, e.g., Sanders-Brown Center on Aging, Department of Pathology]
+    * **Name/Identifier:** UK Alzheimer's Disease Center Neuropathology Whole Slide Image Cohort [BDSA TEST v1.0]
+    * **Source:** [UK-ADRC Neuropathology Lab at the University of Kentucky University of Kentucky](https://neuropathlab.createuky.net/), [PLACEHOLDER: Specific Department, Center, or PI, e.g., Sanders-Brown Center on Aging, Department of Pathology]
     * **Description:** [PLACEHOLDER: Describe the data. E.g., "Digitized whole slide images (WSIs) of human post-mortem brain tissue sections from [number] subjects. Sections were stained with [e.g., Hematoxylin and Eosin (H&E), and immunohistochemistry for Amyloid-beta (Aβ) and phosphorylated Tau (pTau)]. Images were acquired using [e.g., Aperio AT2 scanner at 20x magnification]."]
     * **Preprocessing:** [PLACEHOLDER: Describe significant preprocessing steps. E.g., "WSIs were tiled into non-overlapping [e.g., 224x224 pixel] patches. Tiles with excessive background or artifacts were excluded. Color normalization using [Method, e.g., Macenko method] was applied."]
     * **Annotation (if applicable for supervised fine-tuning or evaluation):** [PLACEHOLDER: Describe the annotation process. E.g., "Regions of interest (ROIs) for [pathologies] were annotated by board-certified neuropathologists. For classification tasks, slide-level or region-level labels for [disease/pathology presence/severity] were provided."]