Model card for phikon-distil-vit-tiny-patch16-224-kather2016

This model is a distilled version of owkin/phikon to a TinyViT on the 1aurent/Kather-texture-2016 dataset.

Model Usage

Image Classification

from transformers import AutoModelForImageClassification, AutoImageProcessor
from urllib.request import urlopen
from PIL import Image

# get example histology image
img = Image.open(
  urlopen(
    "https://datasets-server.huggingface.co/assets/1aurent/Kather-texture-2016/--/default/train/0/image/image.jpg"
  )
)

# load image_processor and model from the hub
model_name = "1aurent/phikon-distil-vit-tiny-patch16-224-kather2016"
image_processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

inputs = image_processor(img, return_tensors="pt")
outputs = model(**inputs)

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}
}