BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
release_r6ughi4ojnh6plpnr7yzt2wjzm
by
Tiange Xiang, Chaoyi Zhang, Dongnan Liu, Yang Song, Heng Huang, Weidong Cai
2020
Abstract
U-Net has become one of the state-of-the-art deep learning-based approaches
for modern computer vision tasks such as semantic segmentation, super
resolution, image denoising, and inpainting. Previous extensions of U-Net have
focused mainly on the modification of its existing building blocks or the
development of new functional modules for performance gains. As a result, these
variants usually lead to an unneglectable increase in model complexity. To
tackle this issue in such U-Net variants, in this paper, we present a novel
Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a
recurrent manner without introducing any extra parameters. Our proposed
bi-directional skip connections can be directly adopted into any
encoder-decoder architecture to further enhance its capabilities in various
task domains. We evaluated our method on various medical image analysis tasks
and the results show that our BiO-Net significantly outperforms the vanilla
U-Net as well as other state-of-the-art methods. Our code is available at
https://github.com/tiangexiang/BiO-Net.
In text/plain
format
Archived Files and Locations
application/pdf 5.4 MB
file_k76zlejg7jaqxgmkhrsrx7iwji
|
arxiv.org (repository) web.archive.org (webarchive) |
2007.00243v1
access all versions, variants, and formats of this works (eg, pre-prints)