Phikon is a self-supervised learning model for histopathology trained with iBOT.
To learn more about how to use the model, we encourage you to read our blog post and view this Colab notebook.
The primary use of the Phikon model can be used for feature extraction from histology image tiles.
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.
All the models we built were trained on the French Jean Zay cluster.
1@article{Filiot2023ScalingSSLforHistoWithMIM,
2 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},
3 title = {Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling},
4 elocation-id = {2023.07.21.23292757},
5 year = {2023},
6 doi = {10.1101/2023.07.21.23292757},
7 publisher = {Cold Spring Harbor Laboratory Press},
8 url = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757},
9 eprint = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757.full.pdf},
10 journal = {medRxiv}
11}
