Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation
<|reference_start|>Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation: Medical image segmentation, a critical application of semantic segmentation in healthcare, has seen significant advancements through specialized computer vision techniques. While deep learning-based medical image segmentation is essential for assisting in medical diagnosis, the lack of diverse training data causes the long-tail problem. Moreover, most previous hybrid CNN-ViT architectures have limited ability to combine various attentions in different layers of the Convolutional Neural Network. To address these issues, we propose a Lagrange Duality Consistency (LDC) Loss, integrated with Boundary-Aware Contrastive Loss, as the overall training objective for semi-supervised learning to mitigate the long-tail problem. Additionally, we introduce CMAformer, a novel network that synergizes the strengths of ResUNet and Transformer. The cross-attention block in CMAformer effectively integrates spatial attention and channel attention for multi-scale feature fusion. Overall, our results indicate that CMAformer, combined with the feature fusion framework and the new consistency loss, demonstrates strong complementarity in semi-supervised learning ensembles. We achieve state-of-the-art results on multiple public medical image datasets. Example code are available at: \url{https://github.com/lzeeorno/Lagrange-Duality-and-CMAformer}.<|reference_end|>
@article{zheng2024lagrange,
    title={Lagrange Duality and Compound Multi-Attention Transformer for
  Semi-Supervised Medical Image Segmentation},
    author={Fuchen Zheng, Quanjun Li, Weixuan Li, Xuhang Chen, Yihang Dong,
  Guoheng Huang, Chi-Man Pun, Shoujun Zhou},
    journal={arXiv preprint arXiv:2409.07793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07793},
    primaryClass={cs.CV cs.AI}
}