radshap 0.0.1

Shapley values for interpreting radiomic models

radshap

This repository proposes a python tool for highlighting the contribution of different regions of interest (ROIs) to the predictions of radiomic models. It estimates the Shapley value of the different ROIs of an image that a trained radiomic model uses to obtain a prediction.

Graphical abstract

a. schematic view of a generic aggregated radiomic model - b. computation of a Shapley value for a specific region.

Install

Install from source

pip install git+https://github.com/ncaptier/radshap.git

Experiments

We provide a jupyter notebook for an illustration with PET images and simple aggregation strategies:

• Classification of Non-Small Cell Lung Cancer subtype and interpretation with Shapley values

We provide a jupyter notebook for an illustration of a robust strategy for computing Shapley values:

• Robust Shapley values for explaining multi-region radiomic models with non-optional regions

Examples

Explanation with Shapley values

import numpy as np
import joblib
from radshap.shapley import Shapley

model = joblib.load("trained_logistic_regression.joblib")
shap = Shapley(predictor = lambda x: model.predict_proba(x)[:, 1], aggregation = ('mean', None))
shapvalues = shap.explain(X) # X a 2D array of shape (n_instances, n_instance_features)

Robust explanation with Shapley values

import numpy as np
import joblib
from radshap.shapley import RobustShapley

model = joblib.load("trained_logistic_regression.joblib")
shap = RobustShapley(predictor = lambda x: model.predict_proba(x)[:, 1],
                     aggregation = ('nanmean', None),
                     background_data = Xback) # Xback a 2D array of shape (n_samples_background, n_input_features)
shapvalues = shap.explain(X) # X a 2D array of shape (n_instances, n_instance_features)

License

This project is licensed under a custom open-source license (see the LICENSE.md file for more details).

Acknowledgements

This package was created as a part of the PhD project of Nicolas Captier in the Laboratory of Translational Imaging in Oncology (LITO) of Institut Curie.