Summary of Lagrange Duality and Compound Multi-attention Transformer For Semi-supervised Medical Image Segmentation, by Fuchen Zheng et al.
Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation
by Fuchen Zheng, Quanjun Li, Weixuan Li, Xuhang Chen, Yihang Dong, Guoheng Huang, Chi-Man Pun, Shoujun Zhou
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medical image segmentation, a critical application of semantic segmentation in healthcare, has seen significant advancements through specialized computer vision techniques. Our paper proposes a novel network that synergizes the strengths of ResUNet and Transformer to address the long-tail problem in medical image segmentation. We introduce CMAformer, which integrates spatial attention and channel attention for multi-scale feature fusion. Additionally, we propose a Lagrange Duality Consistency (LDC) Loss, integrated with Boundary-Aware Contrastive Loss, as the overall training objective for semi-supervised learning. Our results demonstrate strong complementarity in semi-supervised learning ensembles, achieving state-of-the-art results on multiple public medical image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is important in healthcare because it helps doctors diagnose diseases. But, there’s a problem: we don’t have enough training data to make the best models. Our team came up with a new way to combine different parts of an image to help solve this problem. We also created a special kind of network called CMAformer that does a great job at recognizing features in images. When we used our new approach on public medical image datasets, we got better results than anyone else before us. |
Keywords
» Artificial intelligence » Attention » Contrastive loss » Image segmentation » Semantic segmentation » Semi supervised » Transformer
