Explaining machine-learning models for gamma-ray detection and identification
As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281578/?tool=EBI |
| _version_ | 1797797892173332480 |
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| author | Mark S. Bandstra Joseph C. Curtis James M. Ghawaly A. Chandler Jones Tenzing H. Y. Joshi |
| author_facet | Mark S. Bandstra Joseph C. Curtis James M. Ghawaly A. Chandler Jones Tenzing H. Y. Joshi |
| author_sort | Mark S. Bandstra |
| collection | DOAJ |
| description | As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations. |
| first_indexed | 2024-03-13T03:55:13Z |
| format | Article |
| id | doaj.art-c096339ee9864dafb29776bbeeb0c4db |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T03:55:13Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c096339ee9864dafb29776bbeeb0c4db2023-06-22T05:31:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01186Explaining machine-learning models for gamma-ray detection and identificationMark S. BandstraJoseph C. CurtisJames M. GhawalyA. Chandler JonesTenzing H. Y. JoshiAs more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281578/?tool=EBI |
| spellingShingle | Mark S. Bandstra Joseph C. Curtis James M. Ghawaly A. Chandler Jones Tenzing H. Y. Joshi Explaining machine-learning models for gamma-ray detection and identification PLoS ONE |
| title | Explaining machine-learning models for gamma-ray detection and identification |
| title_full | Explaining machine-learning models for gamma-ray detection and identification |
| title_fullStr | Explaining machine-learning models for gamma-ray detection and identification |
| title_full_unstemmed | Explaining machine-learning models for gamma-ray detection and identification |
| title_short | Explaining machine-learning models for gamma-ray detection and identification |
| title_sort | explaining machine learning models for gamma ray detection and identification |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281578/?tool=EBI |
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