XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images
文献类型： 会议论文
第一作者： Yanfeng Zhou
作者： Yanfeng Zhou 1 ; Jiaxing Huang 1 ; Chenlong Wang 2 ; Le Song 2 ; Ge Yang 1 ;
作者机构： 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China|Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.BioMap Research, California, USA
关键词： Training;Solid modeling;Three-dimensional displays;Biological system modeling;Semantic segmentation;Perturbation methods;Artificial neural networks
会议名称： IEEE International Conference on Computer Vision
主办单位：
页码： 21028-21039
摘要： Fully- and semi-supervised semantic segmentation of biomedical images have been advanced with the development of deep neural networks (DNNs). So far, however, DNN models are usually designed to support one of these two learning schemes, unified models that support both fully- and semi-supervised segmentation remain limited. Furthermore, few fully-supervised models focus on the intrinsic low frequency (LF) and high frequency (HF) information of images to improve performance. Perturbations in consistency-based semi-supervised models are often artificially designed. They may introduce negative learning bias that are not beneficial for training. In this study, we propose a wavelet-based LF and HF fusion model XNet, which supports both fully- and semi-supervised semantic segmentation and outperforms state-of-the-art models in both fields. It emphasizes extracting LF and HF information for consistency training to alleviate the learning bias caused by artificial perturbations. Extensive experiments on two 2D and two 3D datasets demonstrate the effectiveness of our model. Code is available at https://github.com/Yanfeng-Zhou/XNet.
分类号： tp391.41-53
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