Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2309.06618v3
• Date: Fri, 15 Dec 2023 23:54:04 GMT
• Title: Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation
• Authors: Yixing Lu, Zhaoxin Fan, Min Xu
• Abstract summary: 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. We propose the Multi-Axis Consistency framework for generating robust pseudo labels.
• Score: 10.628250457432499
• License: http://creativecommons.org/licenses/by/4.0/
• 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 semisupervised learning process. Our extensive experiments on several widelyused datasets unequivocally demonstrate the efficacy of our approach.

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