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.

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🧠 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.

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🔧 Usage: Load as a Vision Encoder

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

import timm
from huggingface_hub import login

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

model = timm.create_model(
    "hf-hub:AI4Pathology/PathOrchestra_V1.0.0.0",
    pretrained=True,
    init_values=1e-5,
    dynamic_img_size=True,
)
model.eval()

🧪 Feature Extraction Example

from PIL import Image
from torchvision import transforms
import torch

# 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 = Image.open("example.png").convert("RGB")
image = transform(image).unsqueeze(0)  # Add batch dimension

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

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📫 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:

@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}
}