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---
license: other
language:
- en
tags:
- biology
---
# Model Card for Phikon
---
## Model Details
Phikon is a self-supervised learning model for histopathology.
To learn more about how to use the model, we encourage you to read our blog post and view this Colab notebook.
### Model Description
- **Developed by:** Owkin
- **Funded by:** Owkin and IDRIS
- **Model type:** Vision Transformer
- **License:** [Owkin non-commercial license](https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt)
## Uses
### Direct Use
The primary use of the Phikon model can be used for feature extraction from histology image tiles.
### Downstream Use
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.
## Technical Specifications
### Compute Infrastructure
All the models we built were trained on the French Jean Zay cluster
### Hardware
NVIDIA V100 GPUs with 32Gb RAM
### Software
PyTorch 1.13.1
### BibTeX entry and citation info
```bibtex
@article{Filiot2023ScalingSSLforHistoWithMIM,
author = {Alexandre Filiot and Ridouane Ghermi and Antoine Olivier and Paul Jacob and Lucas Fidon and Alice Mac Kain and Charlie Saillard and Jean-Baptiste Schiratti},
title = {Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling},
elocation-id = {2023.07.21.23292757},
year = {2023},
doi = {10.1101/2023.07.21.23292757},
publisher = {Cold Spring Harbor Laboratory Press},
url = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757},
eprint = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757.full.pdf},
journal = {medRxiv}
}
```
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