---
license: gpl-3.0
---

# Breast Estrogen Receptor v1 Model Card
This model card describes the model associated with the manuscript "Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology", by Dolezal _et al_, available [here](https://www.nature.com/articles/s41467-022-34025-x).

## Model Details
- **Developed by:** James Dolezal
- **Model type:** Deep convolutional neural network image classifier
- **Language(s):** English
- **License:** GPL-3.0
- **Model Description:** This is a model that can predict, from H&E-stained pathologic images of breast cancer, whether a tumor is likely to be estrogen receptor (ER) negative or positive. It is an [Xception](https://arxiv.org/abs/1610.02357) model with two dropout-enabled hidden layers. 
- **Image processing:** This model expects images of H&E-stained pathology slides at 299 x 299 px and 302 x 302 μm resolution. Images should be stain-normalized using a modified Reinhard normalizer ("Reinhard-Fast") available [here](https://github.com/jamesdolezal/slideflow/blob/master/slideflow/norm/tensorflow/reinhard.py). The stain normalizer should be fit using the `target_means` and `target_stds` listed in the model `params.json` file. Images should be should be standardized with `tf.image.per_image_standardization()`.
- **Resources for more information:** [GitHub Repository](https://github.com/jamesdolezal/histologic-sheep)

# Uses

## Examples
For direct use, the model can be loaded using Tensorflow/Keras:

```
import tensorflow as tf
model = tf.keras.models.load_model('/path/')
```

or loaded with [Slideflow](https://github.com/jamesdolezal/slideflow) version 1.1+ with the following syntax:

```
import slideflow as sf
model = sf.model.load('/path/')
```

The stain normalizer can be loaded and fit using Slideflow:

```
normalizer = sf.util.get_model_normalizer('/path/')
```

The stain normalizer has a native Tensorflow transform and can be directly applied to a tf.data.Dataset:

```
# Map the stain normalizer transformation
# to a tf.data.Dataset
dataset = dataset.map(normalizer.tf_to_tf)
```

Alternatively, the model can be used to generate predictions for whole-slide images processed through Slideflow in an end-to-end [Project](https://slideflow.dev/project_setup.html). To use the model to generate predictions on data processed with Slideflow, simply pass the model to the [`Project.predict()`](https://slideflow.dev/project.html#slideflow.Project.predict) function:

```
import slideflow
P = sf.Project('/path/to/slideflow/project')
P.predict('/model/path')
```

## Direct Use
This model is intended for research purposes only. Possible research areas and tasks include

- Applications in educational settings.
- Research on pathology classification models for breast cancer.

Excluded uses are described below.

### Misuse and Out-of-Scope Use
This model should not be used in a clinical setting to generate predictions that will be used to inform patients, physicians, or any other health care members directly involved in their health care outside the context of an approved research protocol. Using the model in a clinical setting outside the context of an approved research protocol is a misuse of this model. This includes, but is not limited to:

- Generating predictions of images from a patient's tumor and sharing those predictions with the patient
- Generating predictions of images from a patient's tumor and sharing those predictions with the patient's physician, or other members of the patient's healthcare team
- Influencing a patient's health care treatment in any way based on output from this model

### Limitations

The model has not been validated to discriminate estrogen receptor status in a manner which controls for possible underlying biological bias, such tumor grade or histological subtype.

### Bias
This model was trained on The Cancer Genome Atlas (TCGA), which contains patient data from communities and cultures which may not reflect the general population. This datasets is comprised of images from multiple institutions, which may introduce a potential source of bias from site-specific batch effects ([Howard, 2021](https://www.nature.com/articles/s41467-021-24698-1)). 
## Training

**Training Data**
The following dataset was used to train the model:

- The Cancer Genome Atlas (TCGA), BRCA cohort (see next section)

This model was trained on a total of 1,048 slides, with 228 ER-negative tumor and 820 ER-positive tumors.

**Training Procedure**
Each whole-slide image was sectioned into smaller images in a grid-wise fashion in order to extract tiles from whole-slide images at 302 x 302 μm. Image tiles were extracted at the nearest downsample layer, and resized to 299 x 299 px using [Libvips](https://www.libvips.org/API/current/libvips-resample.html#vips-resize). During training,

- Images are stain-normalized with a modified Reinhard normalizer ("Reinhard-Fast"), which excludes the brightness standardization step, available [here](https://github.com/jamesdolezal/slideflow/blob/master/slideflow/norm/tensorflow/reinhard.py)
- Images are randomly flipped and rotated (90, 180, 270)
- Images have a 50% chance of being JPEG compressed with quality level between 50-100%
- Images have a 10% chance of random Gaussian blur, with sigma between 0.5-2.0
- Images are standardized with `tf.image.per_image_standardization()`
- Images are classified through an Xception block, followed by two hidden layers with dropout (p=0.1) enabled during training
- The loss is cross-entropy, with ER-negative=0 and ER-positive=1
- Training is completed after 1 epoch

Additional training information:

- **Hardware:** 1 x A100 GPUs
- **Optimizer:** Adam
- **Batch:** 128
- **Learning rate:** 0.0001, with a decay of 0.98 every 512 steps
- **Hidden layers:** 2 hidden layers of width 1024, with dropout p=0.1

## Evaluation Results
External evaluation results are currently under peer review and will be posted once publicly available.