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AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation by Du, Xiuquan; Ma, Kunpeng; Song, Yuhui is a scholarly article available to read on EtoBox.

What is AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation about?

Automatic and accurate lesion segmentation is critical for clinically estimating the lesion statuses of stroke diseases and developing appropriate diagnostic systems. Although existing methods have achieved remarkable results, further adoption of the models is hindered by: (1) inter-class indistinction, the normal brain tissue resembles the lesion in appearance. (2) intra-class inconsistency, large variability exists between different areas of the lesion. To solve these challenges in stroke segmentation, we propose a novel method, namely Attention Guided Multiscale Recovery framework (AGMR-Net) in this paper. Firstly, a coarse-grained patch attention module in the encoding is adopted to get a patch-based coarse-grained attention map in a multi-stage explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intra-class inconsistency. Secondly, to obtain a more detailed boundary partitioning to solve the challenge of the inter-class indistinction, a newly designed cross-dimensional feature fusion module is used to capture global contextual information to further guide the selective aggregati

Author

Du, Xiuquan; Ma, Kunpeng; Song, Yuhui

Published

2022

Language

EN