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  ```
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  ---
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  ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** {{ developers | default("[More Information Needed]", true)}}
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- - **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
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- - **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
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- - **Model type:** {{ model_type | default("[More Information Needed]", true)}}
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- - **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
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- - **License:** {{ license | default("[More Information Needed]", true)}}
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- - **Finetuned from model [optional]:** {{ finetuned_from | default("[More Information Needed]", true)}}
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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  ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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  ## Technical Specifications
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  ## Model Details
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+ Phikon is a self-supervised learning model for histopathology.
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+ To learn more about how to use the model, we encourage you to read our blog post and view this Colab notebook.
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+ ### Model Description
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+ - **Developed by:** Owkin
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+ - **Funded by:** Owkin and CNRS
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+ - **Model type:** Vision Transformer
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  ## Uses
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  ### Direct Use
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+ The primary use of the Phikon model can be used for feature extraction from histology image tiles.
 
 
 
 
 
 
 
 
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+ ### Downstream Use
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+ The model can be used for cancer classification on a variety of cancer subtypes. The model can also be finetuned to specialise on cancer subtypes.
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  ## Technical Specifications
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