metadata
license: other
language:
- en
tags:
- biology
Model Card for Phikon
BibTeX entry and citation info
@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}
}
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
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