metadata

tags:
  - image-classification
  - timm
  - biology
  - cancer
  - owkin
  - histology
library_name: timm
widget:
  - src: https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif
    example_title: pancancer tile
co2_eq_emissions:
  emissions: 14590
  source: https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2
  training_type: pre-training
  geographical_location: Jean Zay cluster, France (~40 gCO₂eq/kWh)
  hardware_used: 32 V100 32Gb GPUs, 1216 GPU hours

Model card for vit_base_patch16_224.owkin_pancancer

A Vision Transformer (ViT) image classification model.
Trained by Owkin on 40M pan-cancer histology tiles from TCGA.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 85.8
- Image size: 224 x 224 x 3
Papers:
- Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling: https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2
Dataset: TGCA: https://portal.gdc.cancer.gov/
Original: https://github.com/owkin/HistoSSLscaling/
License: https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt

Model Usage

Image Embeddings

from urllib.request import urlopen
from PIL import Image
import timm

# get example histology image
img = Image.open(
  urlopen(
    "https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
  )
)

# load model from the hub
model = timm.create_model(
  model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer",
  pretrained=True,
).eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

Citation

@article {Filiot2023.07.21.23292757,
  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/2023/09/14/2023.07.21.23292757},
  eprint = {https://www.medrxiv.org/content/early/2023/09/14/2023.07.21.23292757.full.pdf},
  journal = {medRxiv}
}