Model Card for Phikon [!IMPORTANT] 🎉 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…
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