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. 2024 Apr 19;14:9013. doi: 10.1038/s41598-024-59248-4

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Sample visual explanations for successful model predictions. Panels (A–C) depict GradCAM activation maps whereas panels (D–F) depict HiResCAM activation maps. Panels in column (C,F) depict activation maps for the same patient ID to enable comparison of the activations maps of the two visual explanation methods for the same image. Pr(class:Normal) and Pr(class:Abnormal) are the probabilities for the Normal and Abnormal class respectively. The color bar units, ranging from 0.0 to 1.0, are unitless and denote the relative contribution of each pixel towards the model’s decision, where blue signifies low or negative importance and red signifies high importance.