---
tags:
- feature-extraction
- image-classification
- timm
- biology
- cancer
- histology
library_name: timm
model-index:
- name: tcga_brca
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: TCGA-BRCA
      type: image-classification
    metrics:
    - type: accuracy
      value: 0.879 ± 0.069
      name: AUC
      verified: false
license: gpl-3.0
pipeline_tag: feature-extraction
inference: false
---

# Model card for resnet50.tcga_brca_simclr

A ResNet50 image classification model. \
Trained on 2M histology patches from TCGA-BRCA.

## Model Details

- **Model Type:** Feature backbone
- **Model Stats:**
  - Params (M): 23.6
  - Image size: 256 x 256 x 3
- **Papers:**
  - Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology: https://arxiv.org/abs/2203.00585
- **Dataset:** TCGA BRCA: https://portal.gdc.cancer.gov/
- **Original:** https://github.com/Richarizardd/Self-Supervised-ViT-Path/
- **License:** [GPLv3](https://github.com/Richarizardd/Self-Supervised-ViT-Path/blob/master/LICENSE)

## Model Usage

### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
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/resnet50.tcga_brca_simclr",
  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))  # output is (batch_size, num_features) shaped tensor
```

## Citation
```bibtex
@misc{chen2022selfsupervised,
  title         = {Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology},
  author        = {Richard J. Chen and Rahul G. Krishnan},
  year          = {2022},
  eprint        = {2203.00585},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV}
}
```