Model card for densenet121.tiatoolbox-kather100k
A DenseNet 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): 7.05
- 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/densenet121.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/densenet121.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}
}