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1aurent
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vit_base_patch16_224.owkin_pancancer

Feature Extraction
timm
PyTorch
Safetensors
image-classification
biology
cancer
owkin
histology
Eval Results
Carbon Emissions
Model card Files Community
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@@ -8,6 +8,163 @@ tags:
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  - owkin
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  - histology
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  library_name: timm
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  widget:
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  - src: https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif
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  example_title: pancancer tile
@@ -20,6 +177,12 @@ co2_eq_emissions:
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  license: other
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  pipeline_tag: feature-extraction
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  inference: false
 
 
 
 
 
 
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  ---
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  # Model card for vit_base_patch16_224.owkin_pancancer
@@ -27,6 +190,8 @@ inference: false
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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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  ## Model Details
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  - **Model Type:** Image classification / feature backbone
 
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  - owkin
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  - histology
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  library_name: timm
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+ model-index:
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+ - name: owkin_pancancer
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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: Camelyon16[Meta]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 94.5 ± 4.4
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+ name: ROC AUC
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+ verified: false
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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: TCGA-BRCA[Hist]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 96.2 ± 3.3
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+ name: ROC AUC
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+ verified: false
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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: TCGA-BRCA[HRD]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 79.3 ± 2.4
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+ name: ROC AUC
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+ verified: false
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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: TCGA-BRCA[Mol]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 81.7 ± 1.6
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+ name: ROC AUC
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+ verified: false
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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: TCGA-BRCA[OS]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 64.7 ± 5.7
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+ name: ROC AUC
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+ verified: false
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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: TCGA-CRC[MSI]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 91.0 ± 2.2
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+ name: ROC AUC
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+ verified: false
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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: TCGA-COAD[OS]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 63.4 ± 7.4
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+ name: ROC AUC
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+ verified: false
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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: TCGA-NSCLC[CType]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 97.7 ± 1.3
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+ name: ROC AUC
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+ verified: false
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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: TCGA-LUAD[OS]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 53.8 ± 4.5
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+ name: ROC AUC
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+ verified: false
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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: TCGA-LUSC[OS]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 62.2 ± 2.9
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+ name: ROC AUC
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+ verified: false
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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: TCGA-OV[HRD]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 74.2± 8.6
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+ name: ROC AUC
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+ verified: false
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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: TCGA-RCC[CType]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 99.5 ± 0.2
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+ name: ROC AUC
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+ verified: false
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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: TCGA-STAD[MSI]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 89.9 ± 3.9
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+ name: ROC AUC
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+ verified: false
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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: TCGA-PAAD[OS]
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 59.2 ± 4.1
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+ name: ROC AUC
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+ verified: false
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  widget:
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  - src: https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif
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  example_title: pancancer tile
 
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  license: other
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  pipeline_tag: feature-extraction
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  inference: false
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+ datasets:
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+ - owkin/camelyon16-features
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+ - owkin/nct-crc-he
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+ - 1aurent/NCT-CRC-HE
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+ metrics:
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+ - roc_auc
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  ---
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  # Model card for vit_base_patch16_224.owkin_pancancer
 
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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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+ ![](https://github.com/owkin/HistoSSLscaling/blob/main/assets/main_figure.png?raw=true)
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+
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  ## Model Details
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  - **Model Type:** Image classification / feature backbone