<p align="center">
<br>
<img src="docs/images/logo.png" alt="ClassyRadiomics">
</p>
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/pwoznicki/ClassyRadiomics/commits/deploy)
[](https://codecov.io/gh/pwoznicki/ClassyRadiomics)
## Simple pipeline for experimenting with radiomics features
| <p align="center"><a href="https://share.streamlit.io/pwoznicki/classyradiomics/main/webapp/app.py"> Streamlit Share | <p align="center"><a href="https://hub.docker.com/repository/docker/piotrekwoznicki/classy-radiomics"> Docker | <p align="center"><a href="https://pypi.org/project/classrad/"> Python |
| -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| <p align="center"><img src="https://github.com/pwoznicki/ClassyRadiomics/raw/main/docs/images/streamlit.png" /></p> | <p align="center"><img src="https://github.com/pwoznicki/ClassyRadiomics/raw/main/docs/images/docker.png"/></p> | <p align="center"><img src="https://github.com/pwoznicki/ClassyRadiomics/raw/main/docs/images/python.png" /></p> |
| <p align="center"><a href="https://share.streamlit.io/pwoznicki/classyradiomics/main/webapp/app.py"> **Demo** | `docker run -p 8501:8501 -v <your_data_dir>:/data -it piotrekwoznicki/classy-radiomics:0.1` | `pip install --upgrade classrad` |
### Installation from source
```bash
git clone https://github.com/pwoznicki/ClassyRadiomics.git
cd ClassyRadiomics
pip install -e .
```
## Example - Hydronephrosis detection from CT images:
### Extract radiomics features and save them to CSV table
```python
df = pd.read_csv(table_dir / "paths.csv")
extractor = FeatureExtractor(
df=df,
out_path=(table_dir / "features.csv"),
image_col="img_path",
mask_col="seg_path",
)
extractor.extract_features()
```
### Create a dataset from the features table
```python
feature_df = pd.read_csv(table_dir / "features.csv")
data = Dataset(
dataframe=feature_df,
features=feature_cols,
target="Hydronephrosis",
task_name="Hydronephrosis detection"
)
data.cross_validation_split_test_from_column(
column_name="cohort", test_value="control"
)
```
### Select classifiers to compare
```python
classifier_names = [
"Gaussian Process Classifier",
"Logistic Regression",
"SVM",
"Random Forest",
"XGBoost",
]
classifiers = [MLClassifier(name) for name in classifier_names]
```
### Create an evaluator to train and evaluate selected classifiers
```python
evaluator = Evaluator(dataset=data, models=classifiers)
evaluator.evaluate_cross_validation()
evaluator.boxplot_by_class()
evaluator.plot_all_cross_validation()
evaluator.plot_test()
```
