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28 March 2023 Multi-task and feature attention for semi supervised medical images segmentation
Congcong Li, Zhaoyang Liu, Jizhe Li, Jinshuo Zhang, Xiuyang Zhao
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Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256623 (2023) https://doi.org/10.1117/12.2667631
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
Semisupervised learning (SSL) algorithms have received extensive attention in medical images segmentation. However, most SSL methods ignore the significance of boundary regions, and they cannot fully extract the significant image features, resulting in unsatisfactory boundaries and nonsmooth objects. In this study, we propose an SSL network, named Multi-Task and Feature Attention network (MTFA-Net). Our network can produce segmentation map and signed distance map (SDM) to fully mine the geometric information of object boundaries. To extract much more meaningful features, we devise a feature attention module and embed it into V-Net to discover the significance of each neuron, strengthen the important features, and suppress secondary features. Our experiments demonstrate that the method can achieve more accurate segmentation results than current methods on 3D left atrial image database.
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Congcong Li, Zhaoyang Liu, Jizhe Li, Jinshuo Zhang, and Xiuyang Zhao "Multi-task and feature attention for semi supervised medical images segmentation", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256623 (28 March 2023); https://doi.org/10.1117/12.2667631
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KEYWORDS
Image segmentation
Medical imaging
Feature extraction
3D modeling
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