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Automatic image segmentation technology is critical to the visual analysis.The autoencoder architecture has satisfying performance in various imagesegmentation tasks. However, autoencoders based on convolutional neuralnetworks (CNN) seem to encounter a bottleneck in improving the accuracy ofsemantic segmentation. Increasing the inter-class distance between foregroundand background is an inherent characteristic of the segmentation network.However, segmentation networks pay too much attention to the main visualdifference between foreground and background, and ignores the detailed edgeinformation, which leads to a reduction in the accuracy of edge segmentation.In this paper, we propose a light-weight end-to-end segmentation frameworkbased on multi-task learning, termed Edge Attention autoencoder Network(EAA-Net), to improve edge segmentation ability. Our approach not only utilizesthe segmentation network to obtain inter-class features, but also applies thereconstruction network to extract intra-class features among the foregrounds.We further design a intra-class and inter-class features fusion module -- I2fusion module. The I2 fusion module is used to merge intra-class andinter-class features, and use a soft attention mechanism to remove invalidbackground information. Experimental results show that our method performs wellin medical image segmentation tasks. EAA-Net is easy to implement and has smallcalculation cost.

EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation