Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
In this paper, we introduce a novel semi-supervised learning framework tailored for medical image segmentation. Central to our approach is the innovative Multi-scale Text-aware ViT-CNN Fusion scheme. This scheme adeptly combines the strengths of both ViTs and CNNs, capitalizing on the unique advantages of both architectures as well as the complementary information in vision-language modalities. Further enriching our framework, we propose the Multi-Axis Consistency framework for generating robust pseudo labels, thereby enhancing the semi-supervised learning process. Our extensive experiments on several widely-used datasets unequivocally demonstrate the efficacy of our approach.
First Page
141
Last Page
155
DOI
10.1007/978-3-031-53308-2_11
Publication Date
1-1-2024
Keywords
Medical image segmentation, Multi-axis consistency, Semi-supervise learning, ViT-CNN fusion
Recommended Citation
Y. Lu et al., "Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14555 LNCS, pp. 141 - 155, Jan 2024.
The definitive version is available at https://doi.org/10.1007/978-3-031-53308-2_11