---
license: cc-by-nc-nd-4.0
language:
- en
pipeline_tag: feature-extraction
library_name: timm
datasets:
- AI4Pathology/pathorchestra-image-features
---
license: cc-by-nc-nd-4.0
language:
  - en
pipeline_tag: feature-extraction
library_name: timm
---

# PathOrchestra_V1.0.0 – Foundation Model for Computational Pathology

## 🔒 Access Policy

Access to the pretrained weights of **PathOrchestra_V1.0.0** is restricted for **academic research purposes only**.  
Please ensure that your Hugging Face account is associated with an **official/institutional email** and request access accordingly.

> **License:** CC BY-NC-ND 4.0 – non-commercial use only; modifications and redistribution are not permitted.

---

## 🧠 Model Overview

**PathOrchestra** is a scalable vision foundation model for computational pathology, pretrained using self-supervised learning on a corpus of **300,000 whole-slide images (WSIs)** spanning **20 organs/tissue types** from multiple medical centers.

The model was evaluated across **112 clinical-grade diagnostic tasks**, leveraging a combination of **61 private** and **51 public datasets**, demonstrating strong generalizability in multi-organ and multi-task settings.

---

## 🔧 Usage: Load as a Vision Encoder

To load the model via `timm` with pretrained weights from the Hugging Face Hub:

```python
import timm
from huggingface_hub import login

# Authenticate with your User Access Token (https://huggingface.co/settings/tokens)
login(token=your_hf_token)

model = timm.create_model(
    "hf-hub:AI4Pathology/PathOrchestra",
    pretrained=True,
    init_values=1e-5,
    dynamic_img_size=True,
)

model.eval()
```


## 🧪 Feature Extraction Example

```python
import torch

from PIL import Image
from torchvision import transforms
from huggingface_hub import hf_hub_download


# Define preprocessing transform
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.485, 0.456, 0.406),
                         std=(0.229, 0.224, 0.225))
])

image_path = hf_hub_download(repo_id="AI4Pathology/PathOrchestra", filename="example.png")
image = Image.open(image_path).convert("RGB")

image = transform(image).unsqueeze(0)  # Add batch dimension

with torch.inference_mode():
    features = model(image)  # Extract patch-level embedding

    print(feature_emb.shape)
```
---

## 📫 Contact
For access requests, collaboration inquiries, or academic use cases, please contact the corresponding authors listed in the official repository.

## 🙏 Acknowledgements
We thank the authors of DINOv2 and UNI for foundational contributions to vision model development.

## 📖 Citation  
If you use PathOrchestra in your research, please cite:

```bibtex
@article{yan2025pathorchestra,
  title={PathOrchestra: A comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks},
  author={Yan, Fang and Wu, Jianfeng and Li, Jiawen and Wang, Wei and Lu, Jiaxuan and Chen, Wen and Gao, Zizhao and Li, Jianan and Yan, Hong and Ma, Jiabo and others},
  journal={arXiv preprint arXiv:2503.24345},
  year={2025}
}
```