Modelpublic

1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung

sourceHugging Faceotherupdated 3y agoView on Hugging Face

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Model Card

Model card for vitbasepatch16224.owkinpancancerftlc25000_lung

A Vision Transformer (ViT) image classification model. \ Trained by Owkin on 40M pan-cancer histology tiles from TCGA. \ Fine-tuned on LC25000's lung subset.

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
—Pretrain Dataset: TGCA: https://portal.gdc.cancer.gov/
—Dataset: LC25000: https://huggingface.co/datasets/1aurent/LC25000
—Original: https://github.com/owkin/HistoSSLscaling/
—License: https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt

Model Usage

Image Classification

python

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

# get example histology image
img = Image.open(
  urlopen(
    "https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg"
  )
)

# load model from the hub
model = timm.create_model(
  model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung",
  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))  # unsqueeze single image into batch of 1

Image Embeddings

python

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

# get example histology image
img = Image.open(
  urlopen(
    "https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg"
  )
)

# load model from the hub
model = timm.create_model(
  model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung",
  pretrained=True,
  num_classes=0,
).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

bibtex

@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}
}