phikon v2 EU AI Act Compliance Profile

Model Card for Phikon-v2

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.

Model Description

• 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

How To Use (Feature Extraction)

The following code snippet allows you to extract features from histology images using Phikon-v2 (CLS token). These features can then be used for downstream applications such as ROI classification (via linear or knn probing), slide classification (via multiple instance learning), segmentation (via ViT-Adapter for instance), etc.

from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel


# Load an image
image = Image.open(
    requests.get(
        "https://github.com/owkin/HistoSSLscaling/blob/main/assets/example.tif?raw=true",
        stream=True
    ).raw
)

# Load phikon-v2
processor = AutoImageProcessor.from_pretrained("owkin/phikon-v2")
model = AutoModel.from_pretrained

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