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--- |
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license: other |
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language: |
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- en |
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tags: |
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- biology |
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- medical |
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- cancer |
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datasets: |
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- owkin/nct-crc-he |
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- owkin/camelyon16-features |
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pipeline_tag: feature-extraction |
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--- |
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# Model Card for Phikon |
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--- |
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Phikon is a self-supervised learning model for histopathology trained with iBOT. |
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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 IDRIS |
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- **Model type:** Vision Transformer |
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- **License:** [Owkin non-commercial license](https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt) |
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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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### Compute Infrastructure |
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All the models we built were trained on the French Jean Zay cluster. |
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### Hardware |
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NVIDIA V100 GPUs with 32Gb RAM |
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### Software |
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PyTorch 1.13.1 |
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--- |
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### BibTeX entry and citation info |
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```bibtex |
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@article{Filiot2023ScalingSSLforHistoWithMIM, |
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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}, |
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title = {Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling}, |
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elocation-id = {2023.07.21.23292757}, |
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year = {2023}, |
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doi = {10.1101/2023.07.21.23292757}, |
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publisher = {Cold Spring Harbor Laboratory Press}, |
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url = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757}, |
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eprint = {https://www.medrxiv.org/content/early/2023/07/26/2023.07.21.23292757.full.pdf}, |
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journal = {medRxiv} |
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} |
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``` |
