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Related Experiment Video

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Explaining machine-learning models for gamma-ray detection and identification.

Mark S Bandstra¹, Joseph C Curtis¹, James M Ghawaly²

¹Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.

|June 20, 2023

Summary

This summary is machine-generated.

Explainable AI methods like SHAP improve understanding of complex gamma-ray spectral analysis models. New techniques enhance model interpretability and accuracy for radiological data applications.

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Area of Science:

Nuclear Physics
Data Science
Artificial Intelligence

Background:

Complex predictive models are increasingly used in gamma-ray spectral analysis.
Explainable Artificial Intelligence (XAI) techniques are emerging for understanding these models.
Gradient-based (e.g., Grad-CAM) and black-box (e.g., LIME, SHAP) methods are being explored.

Purpose of the Study:

To compare and adapt XAI methods for gamma-ray spectral data.
To evaluate the accuracy of different XAI techniques.
To propose novel methods for generating counterfactual explanations.

Main Methods:

Utilized a neural network model trained on synthetic NaI(Tl) urban search data.
Compared gradient-based and black-box XAI methods.
Developed a technique for counterfactual explanations using orthogonal projections.

Main Results:

Black-box methods, LIME and SHAP, demonstrated high accuracy in explaining model predictions.
SHAP is recommended due to its minimal hyperparameter tuning requirements.
A novel technique for generating counterfactual explanations was successfully demonstrated.

Conclusions:

XAI methods can be effectively adapted for gamma-ray spectral analysis.
SHAP offers a robust and accurate solution for model interpretability in this field.
The proposed counterfactual explanation technique enhances understanding of model behavior.