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---
base_model: 1aurent/vit_base_patch16_224.owkin_pancancer
tags:
- image-classification
- timm
- owkin
- biology
- cancer
- lung
library_name: timm
datasets:
- 1aurent/LC25000
metrics:
- accuracy
pipeline_tag: image-classification
model-index:
- name: owkin_pancancer_ft_lc25000_lung
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: 1aurent/LC25000
type: image-classification
metrics:
- type: accuracy
value: 0.999
name: accuracy
verified: false
widget:
- src: >-
https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/5000/image/image.jpg
example_title: benign
- src: >-
https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg
example_title: adenocarcinomas
- src: >-
https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/10000/image/image.jpg
example_title: squamous carcinomas
license: other
license_name: owkin-non-commercial
license_link: https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt
---
# Model card for vit_base_patch16_224.owkin_pancancer_ft_lc25000_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}
}
``` |