Paper Info
Title
Graph-BAS3Net - Boundary-Aware Semi-Supervised Segmentation Network with Bilateral Graph Convolution.
Abstract
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples. However, most of the existing SSLs neglected the geometric shape constraint in object, leading to unsatisfactory boundary and non-smooth of object. In this paper, we propose a novel boundary-aware semi-supervised medical image segmentation network, named Graph-BAS3Net, which incorporates the boundary information and learns duality constraints between semantics and geometrics in the graph domain. Specifically, the proposed method consists of two components: a multi-task learning framework BAS3Net and a graph-based cross-task module BGCM. The BAS3Net improves the existing GAN-based SSL by adding a boundary detection task, which encodes richer features of object shape and surface. Moreover, the BGCM further explores the co-occurrence relations between the semantics segmentation and boundary detection task, so that the network learns stronger semantic and geometric correspondences from both labeled and unlabeled data. Experimental results on the LiTS dataset and COVID-19 dataset confirm that our proposed Graph-BAS3 Net outperforms the state-of-the-art methods in semi-supervised segmentation task.
Year
DOI
Venue
2021
10.1109/ICCV48922.2021.00729
ICCV
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Huimin Huang111.73
Lanfen Lin248.67
Yue Zhang318453.93
Yingying Xu483.62
Jing Zheng500.68
Xiongwei Mao601.35
Xiaohan Qian700.68
Zhiyi Peng8112.32
Jianying Zhou900.34
Yen-Wei Chen10720155.73
Ruofeng Tong1146649.69