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arXiv · 2007.00243

BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

Tiange Xiang·Chaoyi Zhang·Dongnan Liu·Yang Song·Heng Huang·Weidong Cai
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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.

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Tiange Xiang, Chaoyi Zhang, Dongnan Liu, Yang Song, Heng Huang, Weidong Cai. 2020-07-01. BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture. https://arxiv.org/abs/2007.00243

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