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phikon

owkin

> [!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. - **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

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phikon-v2

owkin

Phikon-v2 is a Vision Transformer Large pre-trained with Dinov2 self-supervised method on PANCAN-XL, a dataset of 450M 20x magnification histology images sampled from 60K whole slide images. PANCAN-XL only incorporates publicly available datasets: CPTAC (6,193 WSI) and TCGA (29,502 WSI) for malignant tissue, and GTEx for normal tissue (13,302 WSI). Phikon-v2 improves upon Phikon, our previous foundation model pre-trained with iBOT on 40M histology images from TCGA (6k WSI), on a large variety of weakly-supervised tasks tailored for biomarker discovery. Phikon-v2 is evaluated on external cohorts to avoid any data contamination with PANCAN-XL pre-training dataset, and benchmarked against an exhaustive panel of representation learning and foundation models. - **Developed by:** Owkin, Inc - **Model type:** Pretrained vision backbone (ViT-L/16 via DINOv2) - **Pretraining dataset:** PANCAN-XL, sourced from public histology collections (TCGA, CPTAC, GTEx, TCIA and others). - **Paper:** Arxiv - **License:** Owkin non-commercical licence

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