Update README.md

README.md

@@ -16,15 +16,13 @@ tags:
 * **Model Name:** Med-GLIP
 * **Paper Title:** Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
 * **Authors:** Ziye Deng, Ruihan He, Jiaxiang Liu, Yuan Wang, Zijie Meng, Songtao Jiang, Yong Xie, Zuozhu Liu
-* **Affiliations:** (Not explicitly mentioned in the abstract, but affiliations of the authors would be listed here)
+* **Affiliations:** Zhejiang University
 * **Version:** v1
 * **Date:** (Presumed August 2025)
 * **Model Type:** Medical Language-Image Pre-training Model with Visual Grounding capabilities.
 * **Relevant Links:**
     * arXiv Page: [https://arxiv.org/abs/2508.10528v1](https://arxiv.org/abs/2508.10528v1)
     * DOI: [https://doi.org/10.48550/arXiv.2508.10528](https://doi.org/10.48550/arXiv.2508.10528)
-    * Code Repository: (Add link if available)
-    * Model Weights: (Add link if available)
 * **License:** Creative Commons Attribution 4.0 International (CC BY 4.0)
 * **Citation:**
 
@@ -104,32 +102,3 @@ The model aims to overcome the limitations of existing methods in **fine-grained
 * **Clinical Risk:** The model is an AI research tool and **must not** be used for primary clinical diagnosis or patient care without explicit, strict clinical validation and regulatory approval. Misinterpretation of results could lead to patient harm.
 * **Interpretability:** While the grounding feature aids in interpretability, the overall decision-making process is complex, and failures should be treated with caution.
 * (Refer to the full paper for a detailed discussion of ethical and societal implications.)
-
-## How to Get Started with the Model
-
-(If the model code and weights are released, this section provides usage instructions)
-
-```python
-# Example: Loading the model and processor (assuming compatibility with a library like Hugging Face's transformers)
-
-# from transformers import AutoProcessor, AutoModelForVisualGrounding
-# import torch
-# from PIL import Image
-
-# # Load processor and model
-# processor = AutoProcessor.from_pretrained("your-org/med-glip")
-# model = AutoModelForVisualGrounding.from_pretrained("your-org/med-glip")
-
-# # Prepare input
-# image = Image.open("path/to/medical_image.jpg") 
-# text_query = "evidence of right lower lobe consolidation"
-
-# inputs = processor(images=image, text=text_query, return_tensors="pt")
-
-# # Perform inference
-# with torch.no_grad():
-#     outputs = model(**inputs)
-
-# # Process the output to get bounding boxes (implementation details vary)
-# predicted_boxes = outputs.logits 
-# ...