classrad 0.1

Radiomics-related modules for extraction and experimenting

Simple pipeline for experimenting with radiomics features

Streamlit Share	Docker	Python
Demo	docker run -p 8501:8501 -v <your_data_dir>:/data -it piotrekwoznicki/classy-radiomics:0.1	pip install --upgrade classrad

 

Installation from source

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

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

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

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

evaluator = Evaluator(dataset=data, models=classifiers)
evaluator.evaluate_cross_validation()
evaluator.boxplot_by_class()
evaluator.plot_all_cross_validation()
evaluator.plot_test()