Model Type: Feature backbone
Model Stats:
- Params (M): 23.6
- Image size: 256 x 256 x 3
Papers:
- Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology: https://arxiv.org/abs/2203.00585
Dataset: TCGA BRCA: https://portal.gdc.cancer.gov/
Original: https://github.com/Richarizardd/Self-Supervised-ViT-Path/
License: GPLv3

Model Usage

Image Embeddings

python

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/resnet50.tcga_brca_simclr",
  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

bibtex

@misc{chen2022selfsupervised,
  title         = {Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology},
  author        = {Richard J. Chen and Rahul G. Krishnan},
  year          = {2022},
  eprint        = {2203.00585},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV}
}