README.md

---
language:
- en
tags:
- pathology
- visualgrounding
size_categories:
- 10K<n<100K
---
# PathVG: Pathology Visual Grounding Benchmark

## 📖 Overview  
**PathVG (Pathology Visual Grounding)** is the first large-scale benchmark designed for **linking natural language expressions to precise regions in pathology images**. It enables research at the intersection of computational pathology, computer vision, and natural language processing.

The dataset bridges the gap between **image-level question answering** and **region-level localization**, providing a standardized benchmark for visual grounding in pathology.

The full paper is available on arXiv: [https://arxiv.org/abs/2502.20869](https://arxiv.org/abs/2502.20869)

---

## 📂 Dataset Statistics  
- **Images**: 27,610 pathology images  
- **Annotations**: 33,500 expert-verified expressions with bounding boxes  
- **Magnifications**:  
  - **20×**: Focus on cell arrangement and interactions  
  - **40×**: Emphasize cellular structure and tumor infiltration  
- **Annotation Quality**: All expressions are created and validated by pathology experts via multi-stage review  

---

## 📊 Data Format  
Each sample contains:  
- `image_id`: Image filename (unique identifier)  
- `height`: Image height in pixels  
- `width`: Image width in pixels  
- `bbox`: Bounding box coordinates in format `[x_min, y_min, x_max, y_max]`  
- `bbox_id`: Unique bounding box identifier within the image  
- `expression`: List of natural language descriptions associated with the bounding box

Example:
```json
{
  "bbox": [1680, 603, 1818, 756],
  "image_id": "8006.jpg",
  "height": 2048,
  "width": 2048,
  "bbox_id": 1,
  "expression": [
    "Upper-right image shows distinct tumor cell cluster spreading."
  ]
}