Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, saliency maps; biomarkers
Motivation: It is significant to understand if saliency maps can be considered as potential biomarkers by providing reliable anatomical information in 3D medical imaging classification.
Goal(s): We found saliency maps can provide different (even mutually exclusive) information with randomised models. It is necessary to estimate the robustness of saliency maps under the stochastic training process.
Approach: We introduced a novel method by re-organising the saliency scores in the saliency maps and quantify the inter-map difference for estimating the robustness of saliency maps.
Results: All selected explanation methods were not able to exhibit strong performance in the estimation of robustness of saliency maps.
Impact: Our estimation provides evidence that saliency maps are not competent to maintain the robustness under the stochastic training process. Researchers should be critically careful when utilising saliency maps as biomarkers for interpretation.
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