Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2508.13712v1
• Date: Tue, 19 Aug 2025 10:19:15 GMT
• Title: Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
• Authors: Shumeng Li, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao,
• Abstract summary: We propose a novel Diversity-enhanced Collaborative Mamba framework for semi-supervised medical image segmentation.<n>From the data perspective, we develop patch-level weak-strong mixing augmentation with Mamba's scanning modeling characteristics.<n>From the network perspective, we introduce a diverse-scan collaboration module, which could benefit from the prediction discrepancies arising from different scanning directions.
• Score: 43.28818131076307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to generate pseudo labels. Recently, advanced state space models, represented by Mamba, have shown efficient handling of long-range dependencies. This drives us to explore their potential in semi-supervised medical image segmentation. In this paper, we propose a novel Diversity-enhanced Collaborative Mamba framework (namely DCMamba) for semi-supervised medical image segmentation, which explores and utilizes the diversity from data, network, and feature perspectives. Firstly, from the data perspective, we develop patch-level weak-strong mixing augmentation with Mamba's scanning modeling characteristics. Moreover, from the network perspective, we introduce a diverse-scan collaboration module, which could benefit from the prediction discrepancies arising from different scanning directions. Furthermore, from the feature perspective, we adopt an uncertainty-weighted contrastive learning mechanism to enhance the diversity of feature representation. Experiments demonstrate that our DCMamba significantly outperforms other semi-supervised medical image segmentation methods, e.g., yielding the latest SSM-based method by 6.69% on the Synapse dataset with 20% labeled data.

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