论文标题
MA-UNET：基于多尺度和注意力图像分段的改进版本的UNET版本
MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation
论文作者
论文摘要
尽管卷积神经网络（CNN）正在促进医学图像语义分割的发展，但标准模型仍然存在一些缺点。首先，跳过连接操作中的编码器和解码器子网络的功能映射具有较大的语义差异。其次，远程功能依赖性未有效建模。第三，忽略了不同量表的全局上下文信息。在本文中，我们试图通过添加注意门（AGS）来消除跳过连接操作中的语义歧义，并使用注意机制将局部特征及其相应的全局依赖性结合在一起，明确地对频道之间的依赖性进行模型，并使用多规模的预测性融合来利用不同规模的全局信息。与其他最先进的分割网络相比，我们的模型在引入更少的参数的同时获得了更好的分割性能。
Although convolutional neural networks (CNNs) are promoting the development of medical image semantic segmentation, the standard model still has some shortcomings. First, the feature mapping from the encoder and decoder sub-networks in the skip connection operation has a large semantic difference. Second, the remote feature dependence is not effectively modeled. Third, the global context information of different scales is ignored. In this paper, we try to eliminate semantic ambiguity in skip connection operations by adding attention gates (AGs), and use attention mechanisms to combine local features with their corresponding global dependencies, explicitly model the dependencies between channels and use multi-scale predictive fusion to utilize global information at different scales. Compared with other state-of-the-art segmentation networks, our model obtains better segmentation performance while introducing fewer parameters.
