Divide, Conquer, and Aggregate: Asymmetric Experts for Class-Imbalanced Semi-Supervised Medical Image Segmentation

Abstract: Semi-supervised medical image segmentation (SSMIS) aims to alleviate annotation scarcity, but general methods, often developed on few-class datasets, suffer performance degradation in class-imbalanced multi-organ scenarios. Existing class-imbalanced SSMIS methods also struggle, as their single-decoder architecture is forced to handle vastly different scales with shared parameters. This process is easily dominated by majority classes, fundamentally limiting tail-class segmentation capability. To address this, we propose a ``Divide, Conquer, and Aggregate" (DCA) framework, featuring a unified encoder, three expert decoders, and an aggregation decoder. First, we Divide by applying a Logarithmic Gap Analysis to statically partition foreground classes into stable Head, Medium, and Tail sets, which aligns with anatomical priors. Then, we Conquer by training the three architecturally asymmetric experts independently using a label-split strategy. This fundamentally alleviates the burden on a single decoder. The experts' predictions on unlabeled data are fused via logit stitching to generate high-quality pseudo-labels. Finally, we Aggregate using an aggregation decoder with a Dynamic Feature Aggregation Module (DFAM), which dynamically fuses priors from all three experts to achieve unbiased predictions and fully leverage unlabeled data. Experiments demonstrate that our DCA framework significantly outperforms state-of-the-art general and class-imbalanced SSMIS methods.

• [Mar 2026] Our paper has been accepted by CVPR 2026! 🎉
• [Coming Soon] The complete PyTorch implementation will be released here. Stay tuned!

We are currently organizing the code and will release it step by step:

• Release dataset preprocessing scripts (BTCV, BUSI, etc.).
• Release the core implementation of the DCA framework.
• Provide training and evaluation scripts.
• Upload pre-trained weights.

If you find our work or this repository useful, please consider citing our paper:

@inproceedings{liu2026divide,
  title={Divide, Conquer, and Aggregate: Asymmetric Experts for Class-Imbalanced Semi-Supervised Medical Image Segmentation},
  author={Liu, Yajun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}