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R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation
Security and Communication Networks Pub Date : 2021-06-10 , DOI: 10.1155/2021/6625688
Qiang Zuo 1 , Songyu Chen 1 , Zhifang Wang 1
Affiliation  

In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.

中文翻译:

R2AU-Net:用于多模态医学图像分割的注意力循环残差卷积神经网络

近年来,基于深度学习的语义分割方法在医学图像分割中提供了先进的性能。作为典型的分割网络之一,U-Net 成功应用于多模态医学图像分割。本文提出了一种基于U-Net的具有注意力门连接的循环残差卷积神经网络(R2AU-Net)。它通过用循环残差卷积单元替换 U-Net 中的基本卷积单元来增强整合上下文信息的能力。此外,R2AU-Net 采用注意力门而不是原始的跳过连接。在本文中,实验是在三个多模式数据集上进行的:ISIC 2018、DRIVE 以及 LUNA 和 Kaggle Data Science Bowl 2017 中使用的公共数据集。
更新日期:2021-06-10
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