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EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation
[article]
2022
arXiv
pre-print
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
arXiv:2208.09197v1
fatcat:q2hvbrnbijhdlaeebknynlbhsm