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This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the KRONOS model and its derivatives, which include models trained on outputs from the KRONOS model or datasets created from the KRONOS model, is prohibited and requires prior approval. Please note that the primary email used to sign up for your Hugging Face account must match your institutional email to receive approval. By downloading the model, you attest that all information (affiliation, research use) is correct and up-to-date. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the KRONOS model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.
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Model Card for KRONOS
What is KRONOS?
KRONOS is a panel-agnostic foundation model for spatial proteomics, self-supervised on 47 million single-marker patches spanning 175 protein markers, 16 tissue types, 8 imaging platforms and 5 institutions. Its architecture couples a shared channel-wise stem with sinusoidal marker-identity embeddings, making it natively compatible with high-dimensional multiplex data.
Copyright & Licence
The KRONOS codebase and pretrained weights are released to the academic community for non-commercial academic research only. Any commercial research use, integration into commercial products or services, or creation of derivative models (including those further trained, fine-tuned, or distilled from KRONOS) requires prior approvals.
Requesting Access
As mentioned in the gated prompt, you must agree to the outlined terms of use, with the primary email for your HuggingFace account matching your institutional email. If your primary email is a personal email (@gmail/@hotmail/@qq) your request will be denied. To fix this, you can: (1) add your official institutional email to your HF account, and confirm your email address to verify, and (2) set your institutional email as your primary email in your HF account. Other reasons for your request access being denied include other mistakes in the form submitted, for example: full name includes abbreviations, affiliation is not spelled out, the described research use is not sufficient, or email domain address not recognized.
Model Description
- Developed by: Mahmood Lab AI for Pathology @ Harvard/BWH
- Model type: Multiplex Vision Transformer-Small
- Pretraining dataset: 47 million patches from multiplex spatial proteomic images
- Repository: https://github.com/mahmoodlab/KRONOS
- Paper: TBD
- License: CC-BY-NC-ND-4.0
License and Terms of Use
This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the KRONOS model and its derivatives, which include models trained on outputs from the KRONOS model or datasets created from the KRONOS model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the KRONOS model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.
Contact
For any additional questions or comments, contact Faisal Mahmood (faisalmahmood@bwh.harvard.edu), or Muhammad Shaban (mshaban@bwh.harvard.edu).
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