{
  "id": "2409.07793",
  "version": "v1",
  "published": "2024-09-12T06:52:46.000Z",
  "updated": "2024-09-12T06:52:46.000Z",
  "title": "Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation",
  "authors": [
    "Fuchen Zheng",
    "Quanjun Li",
    "Weixuan Li",
    "Xuhang Chen",
    "Yihang Dong",
    "Guoheng Huang",
    "Chi-Man Pun",
    "Shoujun Zhou"
  ],
  "comment": "5 pages, 4 figures, 3 tables",
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "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}.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-09-12T06:52:46.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "semi-supervised medical image segmentation",
      "compound multi-attention transformer",
      "lagrange duality",
      "effectively integrates spatial attention",
      "learning-based medical image segmentation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}