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:
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
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:
@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}
}