  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.
