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--- |
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base_model: 1aurent/vit_base_patch16_224.owkin_pancancer |
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tags: |
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- image-classification |
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- timm |
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- owkin |
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- biology |
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- cancer |
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- lung |
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library_name: timm |
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datasets: |
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- 1aurent/LC25000 |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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model-index: |
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- name: owkin_pancancer_ft_lc25000_lung |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: 1aurent/LC25000 |
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type: image-classification |
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metrics: |
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- type: accuracy |
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value: 0.999 |
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name: accuracy |
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verified: false |
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widget: |
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- src: >- |
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https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/5000/image/image.jpg |
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example_title: benign |
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- src: >- |
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https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg |
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example_title: adenocarcinomas |
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- src: >- |
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https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/10000/image/image.jpg |
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example_title: squamous carcinomas |
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license: other |
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license_name: owkin-non-commercial |
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license_link: https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt |
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--- |
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# Model card for vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung |
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A Vision Transformer (ViT) image classification model. \ |
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Trained by Owkin on 40M pan-cancer histology tiles from TCGA. \ |
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Fine-tuned on LC25000's lung subset. |
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## Model Details |
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|
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Params (M): 85.8 |
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- Image size: 224 x 224 x 3 |
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- **Papers:** |
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- Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling: https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2 |
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- **Pretrain Dataset:** TGCA: https://portal.gdc.cancer.gov/ |
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- **Dataset:** LC25000: https://huggingface.co/datasets/1aurent/LC25000 |
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- **Original:** https://github.com/owkin/HistoSSLscaling/ |
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- **License:** https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt |
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|
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## Model Usage |
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|
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### Image Classification |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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# get example histology image |
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img = Image.open( |
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urlopen( |
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"https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg" |
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) |
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) |
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|
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# load model from the hub |
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model = timm.create_model( |
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model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung", |
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pretrained=True, |
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).eval() |
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|
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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``` |
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|
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### Image Embeddings |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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# get example histology image |
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img = Image.open( |
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urlopen( |
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"https://datasets-server.huggingface.co/assets/1aurent/LC25000/--/default/train/0/image/image.jpg" |
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) |
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) |
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|
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# load model from the hub |
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model = timm.create_model( |
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model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_lung", |
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pretrained=True, |
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num_classes=0, |
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).eval() |
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|
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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``` |
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## Citation |
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```bibtex |
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@article {Filiot2023.07.21.23292757, |
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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}, |
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title = {Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling}, |
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elocation-id = {2023.07.21.23292757}, |
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year = {2023}, |
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doi = {10.1101/2023.07.21.23292757}, |
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publisher = {Cold Spring Harbor Laboratory Press}, |
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URL = {https://www.medrxiv.org/content/early/2023/09/14/2023.07.21.23292757}, |
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eprint = {https://www.medrxiv.org/content/early/2023/09/14/2023.07.21.23292757.full.pdf}, |
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journal = {medRxiv} |
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} |
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``` |
