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models Virchow
Virchow is a self-supervised vision transformer pretrained using 1.5M whole slide histopathology images. The model can be used as a tile-level feature extractor (frozen or finetuned) to achieve state-of-the-art results for a wide variety of downstream computational pathology use cases.
Developed by: Paige, NYC, USA and Microsoft Research, Cambridge, MA USA Model Type: Image feature backbone Model Stats: Params (M): 632 Image size: 224 x 224 Model Architecture: Architecture: ViT-H/14 Patch size: 14 Layers: 32 Embedding dimension: 1280 Activation function: SwiGLU Attention heads: 16 LayerScale: true Training Details: Precision: Mixed precision (fp16) Objective: Modified DINOv2 (https://doi.org/10.48550/arXiv.2304.07193) Paper: A foundation model for clinical-grade computational pathology and rare cancers detection: https://www.nature.com/articles/s41591-024-03141-0 Pretraining Dataset: Internal dataset of 1.5 million whole slide images from Memorial Sloan Kettering Cancer Center, tiles sampled at 0.5 microns per pixel resolution (20x magnification). License: Apache 2.0
Direct use Virchow intended to be used as a frozen feature extractor as the foundation for tile-level and whole slide-level classifiers.
Downstream use Virchow can be finetuned to adapt to specific tasks and/or datasets.
Terms
The Virchow Model and associated code are released under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Additional Terms
By downloading the Virchow Model, you attest that all account information (affiliation, research use) is correct and up-to-date. Downloading the Virchow Model requires prior registration on Azure AI Studio and agreeing to the terms of use.
While the Apache 2.0 License grants broad permissions, we kindly request that users adhere to the following guidelines:
Attribution: We encourage proper attribution when using or redistributing the Virchow Model or its derivatives. Please include a reference to the original source and creators.
Responsible Use: Users are expected to use the Virchow Model responsibly and ethically. Please consider the potential impacts of your use on individuals and society.
Medical or Clinical Use: The Virchow Model is not intended for use in medical diagnosis, treatment, or prevention of disease of real patients. It should not be used as a substitute for professional medical advice.
Privacy and Data Protection: Users should respect privacy rights and comply with applicable data protection laws when using the Virchow Model.
No Malicious Use: The Virchow Model should not be used to create malicious code, malware, or to interfere with the proper functioning of computer systems.
Transparency: If you use the Virchow Model in a product or service, we encourage you to disclose this fact to your end users.
Feedback and Contributions: We welcome feedback and contributions to improve the Virchow Model. Please consider sharing your improvements with the community.
These additional terms are not intended to restrict your rights under the Apache 2.0 License but to promote responsible and ethical use of the Virchow Model.
By using the Virchow Model, you acknowledge that you have read and understood these terms.
Citation
Please cite the following work if you used this model in your research.
Vorontsov, E., Bozkurt, A., Casson, A. et al. A foundation model for clinical-grade computational pathology and rare cancers detection. Nat Med (2024). https://doi.org/10.1038/s41591-024-03141-0
@article{vorontsov2024virchow,
title={A foundation model for clinical-grade computational pathology and rare cancers detection},
author={Vorontsov, Eugene and Bozkurt, Alican and Casson, Adam and Shaikovski, George and Zelechowski, Michal and Severson, Kristen and Zimmermann, Eric and Hall, James and Tenenholtz, Neil and Fusi, Nicolo and Yang, Ellen and Mathieu, Philippe and van Eck, Alexander and Lee, Donghun and Viret, Julian and Robert, Eric and Wang, Yi Kan and Kunz, Jeremy D. and Lee, Matthew C. H. and Bernhard, Jan H. and Godrich, Ran A. and Oakley, Gerard and Millar, Ewan and Hanna, Matthew and Wen, Hannah and Retamero, Juan A. and Moye, William A. and Yousfi, Razik and Kanan, Christopher and Klimstra, David S. and Rothrock, Brandon and Liu, Siqi and Fuchs, Thomas J.},
journal={Nature Medicine},
year={2024},
publisher={Nature Publishing Group}
}
{
"input_data": {
"columns": [
"image"
],
"index":[0],
"data": [
["image1"]
]
}
}Note:
- "image1" and "image2" should be publicly accessible urls or strings in base64 format.
[
{
"output": [
0.0, 0.0, 0.0, 0.0
]
}
]Output will be image embeddings.
Version: 2
task : image-feature-extraction industry : health-and-life-sciences licenseDescription : Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity; "control" means (i) the power to direct or manage the entity, (ii) ownership of 50% or more of shares, or (iii) beneficial ownership. "You" or "Your" means an individual or Legal Entity exercising permissions under this License. "Source" form means the preferred form for making modifications, including source code, documentation source, and configuration files. "Object" form means any form resulting from mechanical transformation of a Source form, including object code, generated documentation, and conversions to other media types. "Work" means the work of authorship made available under the License. "Derivative Works" means any work based on the Work with editorial revisions or modifications representing an original authorship; excludes works merely linking to the Work. "Contribution" means any work submitted to Licensor for inclusion in the Work. "Contributor" means Licensor and any entity whose Contribution is included in the Work. 2. Grant of Copyright License: each Contributor grants You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work in Source or Object form. 3. Grant of Patent License: each Contributor grants You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable patent license to make, use, sell, import, and otherwise transfer the Work; this license terminates if You initiate patent litigation alleging the Work infringes a patent. 4. Redistribution: You may distribute the Work or Derivative Works if You (a) provide a copy of this License, (b) state changes made, (c) retain copyright/patent/trademark notices, and (d) include NOTICE file contents when applicable. You may add Your own attribution notices. 5. Submission of Contributions: unless stated otherwise, Contributions submitted are under this License. 6. Trademarks: this License does not grant rights to use Licensor’s trademarks. 7. Disclaimer of Warranty: the Work is provided "AS IS" without warranties or conditions of any kind, including title, non-infringement, merchantability, or fitness for a purpose. 8. Limitation of Liability: Contributors are not liable for any damages arising from use of the Work. 9. Accepting Warranty or Additional Liability: You may offer support/warranty/indemnity for a fee, but only on Your own behalf and must indemnify Contributors. END OF TERMS AND CONDITIONS Preview inference_supported_envs : ['hf'] license : apache-2.0 author : Paige hiddenlayerscanned SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']
View in Studio: https://ml.azure.com/registries/azureml/models/Virchow/version/2
License: apache-2.0
inference-min-sku-spec: 6|1|112|64
inference-recommended-sku: Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2
languages: en
SharedComputeCapacityEnabled: True