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. 2022 Nov 21;13(2):572–584. doi: 10.21037/qims-22-531

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2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

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Representative cases of (A) original chest Radiograph images; (B) lung region activation maps generated by the proposed network; (C) lung region activation maps overlaid on the chest Radiograph images; (D) binary mask of the region of interest. Colors represent the importance scores, which represent the importance of each pixel in a feature map towards the overall classification decision. The lung region which has a value greater than 0.2 is segmented as the ROI. From top to bottom, the three cases were from normal class, non-COVID-19 pneumonia class and COVID-19 class respectively. NCP, non-COVID-19 pneumonia; COVID-19, coronavirus disease 2019; ROI, region of interest.