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  ---
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  tags:
 
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  - image-classification
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  - timm
 
 
 
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  library_name: timm
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- license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model card for vit_small_patch16_256.tcga_brca_dino
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  tags:
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+ - feature-extraction
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  - image-classification
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  - timm
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+ - biology
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+ - cancer
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+ - histology
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  library_name: timm
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+ model-index:
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+ - name: tcga_brca
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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: TCGA-BRCA
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+ type: image-classification
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+ metrics:
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+ - type: accuracy
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+ value: 0.886 ± 0.059
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+ name: AUC
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+ verified: false
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+ license: gpl-3.0
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+ pipeline_tag: feature-extraction
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+ inference: false
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  ---
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+
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+ # Model card for resnet50.tcga_brca_simclr
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+
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+ A Vision Transformer (ViT) image classification model. \
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+ Trained on 2M histology patches from TCGA-BRCA.
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+
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+ ![](https://github.com/Richarizardd/Self-Supervised-ViT-Path/raw/master/.github/Pathology_DINO.jpg)
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+
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+ ## Model Details
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+
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+ - **Model Type:** Feature backbone
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+ - **Model Stats:**
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+ - Params (M): 21.7
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+ - Image size: 256 x 256 x 3
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+ - **Papers:**
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+ - Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology: https://arxiv.org/abs/2203.00585
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+ - **Dataset:** TGCA BRCA: https://portal.gdc.cancer.gov/
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+ - **Original:** https://github.com/Richarizardd/Self-Supervised-ViT-Path/
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+ - **License:** [GPLv3](https://github.com/Richarizardd/Self-Supervised-ViT-Path/blob/master/LICENSE)
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+
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+ ## Model Usage
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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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+
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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://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
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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_small_patch16_256.tcga_brca_dino",
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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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+
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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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+
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+ ## Citation
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+ ```bibtex
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+ @misc{chen2022selfsupervised,
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+ title = {Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology},
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+ author = {Richard J. Chen and Rahul G. Krishnan},
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+ year = {2022},
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+ eprint = {2203.00585},
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+ archiveprefix = {arXiv},
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+ primaryclass = {cs.CV}
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+ }
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+ ```