metadata

license: other
language:
  - en
tags:
  - biology
  - medical
  - cancer
datasets:
  - owkin/nct-crc-he
  - owkin/camelyon16-features
pipeline_tag: feature-extraction

Model Card for Phikon

🎉 Check out the latest version of Phikon here: Phikon-v2

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.

Model Description

Developed by: Owkin
Funded by: Owkin and IDRIS
Model type: Vision Transformer Base
Model Stats:
- Params (M): 85.8
- Image size: 224 x 224 x 3
Paper:
- Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling. A. Filiot et al., medRxiv 2023.07.21.23292757; doi: https://doi.org/10.1101/2023.07.21.23292757
Pretrain Dataset: 40 million pan-cancer tiles extracted from TGCA
Original: https://github.com/owkin/HistoSSLscaling/
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

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}
}