metadata

tags:
  - image-classification
  - feature-extraction
  - timm
  - biology
  - cancer
  - histology
  - TIA
  - tiatoolbox
library_name: timm
pipeline_tag: image-classification
license: cc-by-4.0
datasets:
  - 1aurent/NCT-CRC-HE
widget:
  - src: >-
      https://datasets-server.huggingface.co/assets/1aurent/NCT-CRC-HE/--/default/CRC_VAL_HE_7K/0/image/image.jpg
    example_title: debris

Model card for mobilenetv2_100.tiatoolbox-kather100k

A MobileNet-v2 image classification model.
Trained by Tissue Image Analytics (TIA) Centre on "kather100k" histology patches.

Model Details

Model Type: Image classification / Feature backbone
Model Stats:
- Params (M): 2.27
- Image size: 224 x 224 x 3
Dataset: kather100k, also called NCT-CRC-HE
Original: https://github.com/TissueImageAnalytics/tiatoolbox
License: Creative Commons Attribution 4.0 International

Model Usage

Image Classification

from urllib.request import urlopen
from PIL import Image
import torch.nn as nn
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/mobilenetv2_100.tiatoolbox-kather100k",
  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)

data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data)  # output is a (batch_size, num_features) shaped tensor

Image Embeddings

from urllib.request import urlopen
from PIL import Image
import torch.nn as nn
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/mobilenetv2_100.tiatoolbox-kather100k",
  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)

data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data)  # output is a (batch_size, num_features) shaped tensor

Citation

@article{Pocock2022,
  author    = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
  doi       = {10.1038/s43856-022-00186-5},
  issn      = {2730-664X},
  journal   = {Communications Medicine},
  month     = {sep},
  number    = {1},
  pages     = {120},
  publisher = {Springer US},
  title     = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
  url       = {https://www.nature.com/articles/s43856-022-00186-5},
  volume    = {2},
  year      = {2022}
}