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. 2021 Dec 16;1:61. doi: 10.1038/s43856-021-00062-8

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© The Author(s) 2021

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a The architecture of the DAGMNet. In DAG, sAG stands for spatial attention gate and cAG stands for channel attention gate. “N_f" denotes the number features (N_f = 32 for our final deployed DAGMNet). b Flowchart of the input image’s dimension for training networks and flowchart of the lesion predict’s dimension for inferencing networks in IP-MNI (high-resolution) space.