论文标题
EAA-NET：使用阶级内部功能进行医学图像分割重新考虑自动编码器体系结构
EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation
论文作者
论文摘要
自动图像分割技术对于视觉分析至关重要。自动编码器体系结构在各种图像分割任务中具有令人满意的性能。但是，基于卷积神经网络（CNN）的自动编码器似乎在提高语义分割的准确性方面遇到了瓶颈。增加前景和背景之间的类间距离是分割网络的固有特征。但是，分割网络过于关注前景和背景之间的主要视觉差异，而忽略了详细的边缘信息，从而导致边缘分割的准确性降低。在本文中，我们提出了一个基于多任务学习的轻量级端到端细分框架，称为Edge Coasity Authosocododer Network（EAA-NET），以提高边缘细分能力。我们的方法不仅利用分割网络来获得类间特征，而且还采用重建网络来提取前景中的类内特征。我们进一步设计了一个阶层和类间特征融合模块-I2融合模块。 I2融合模块用于合并课内和类间特征，并使用软注意机制来删除无效的背景信息。实验结果表明，我们的方法在医疗图像分割任务中的表现良好。 EAA-NET易于实现，并且计算成本较小。
Automatic image segmentation technology is critical to the visual analysis. The autoencoder architecture has satisfying performance in various image segmentation tasks. However, autoencoders based on convolutional neural networks (CNN) seem to encounter a bottleneck in improving the accuracy of semantic segmentation. Increasing the inter-class distance between foreground and background is an inherent characteristic of the segmentation network. However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation. In this paper, we propose a light-weight end-to-end segmentation framework based on multi-task learning, termed Edge Attention autoencoder Network (EAA-Net), to improve edge segmentation ability. Our approach not only utilizes the segmentation network to obtain inter-class features, but also applies the reconstruction network to extract intra-class features among the foregrounds. We further design a intra-class and inter-class features fusion module -- I2 fusion module. The I2 fusion module is used to merge intra-class and inter-class features, and use a soft attention mechanism to remove invalid background information. Experimental results show that our method performs well in medical image segmentation tasks. EAA-Net is easy to implement and has small calculation cost.
