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This model and associated code are released under the Apache-2.0 license.
This model is provided “as-is” without warranties of any kind, express or implied. This model has not been reviewed, certified, or approved by any regulatory body, including but not limited to the FDA (U.S.), EMA (Europe), MHRA (UK), or other medical device authorities. Any application of this model in healthcare or biomedical settings must comply with relevant regulatory requirements and undergo independent validation. Users assume full responsibility for how they use this model and any resulting consequences. The authors, contributors, and distributors disclaim any liability for damages, direct or indirect, resulting from model use. Users are responsible for ensuring compliance with data protection regulations (e.g., GDPR, HIPAA) when using it in research that involves patient data.

Model card for H-optimus-0

H-optimus-0 is an open-source foundation model for histology, developed by Bioptimus. The model is a 1.1B parameter vision transformer trained on a proprietary collection of more than 500,000 H&E stained whole slide histology images. For more information, please refer to our GitHub repository here.

H-optimus-0 can be used to extract powerful features from histology images for various downstream applications, such as mutation prediction, survival analysis, or tissue classification.

How to use it to extract features.

The code below can be used to run inference; H-optimus-0 expects images of size 224x224 that were extracted at 0.5 microns per pixel.

from huggingface_hub import login
import torch
import timm 
from torchvision import transforms

# Login to the Hugging Face hub, using your user access token that can be found here:
# https://huggingface.co/settings/tokens.
login()

model = timm.create_model(
    "hf-hub:bioptimus/H-optimus-0", pretrained=True, init_values=1e-5, dynamic_img_size=False
)
model.to("cuda")
model.eval()

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=(0.707223, 0.578729, 0.703617), 
        std=(0.211883, 0.230117, 0.177517)
    ),
])

input = torch.rand(3, 224, 224)
input = transforms.ToPILImage()(input)

# We recommend using mixed precision for faster inference.
with torch.autocast(device_type="cuda", dtype=torch.float16):
    with torch.inference_mode():
        features = model(transform(input).unsqueeze(0).to("cuda"))

assert features.shape == (1, 1536)

BibTeX entry and citation info.

If you find this repository useful, please consider citing our work:

@software{hoptimus0,
  author = {Saillard, Charlie and Jenatton, Rodolphe and Llinares-López, Felipe and Mariet, Zelda and Cahané, David and Durand, Eric and Vert, Jean-Philippe},
  title = {H-optimus-0},
  url = {https://github.com/bioptimus/releases/tree/main/models/h-optimus/v0},
  year = {2024},
}

bioptimus
/

H-optimus-0

You need to agree to share your contact information to access this model

Model card for H-optimus-0

How to use it to extract features.

BibTeX entry and citation info.

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