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. 2024 Jun 27;14(7):4825–4839. doi: 10.21037/qims-23-1747

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2024 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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The tumor segmentation map of ResNet-18. (A) Original input image, (B) the given calibrated label, (C) the segmentation map generated by U-Net, (D) the segmentation map generated by Trans U-Net, (E) the segmentation map generated by attention U-Net, (F) the segmentation map generated by Dense U-Net, and (G) the segmentation map generated by AGCAF-Net. The red box indicates the part of the segmentation result that differed between the methods, and the green box indicates the part of the segmentation result that is incorrect. AGCAF-Net, attention-guided context asymmetric fusion network.