Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
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View abstract on PubMed
Summary
This summary is machine-generated.We introduce DCMamba, a novel framework for semi-supervised medical image segmentation. It significantly improves segmentation accuracy by leveraging data, network, and feature diversity, outperforming existing methods.
Area Of Science
- Medical image analysis
- Computer vision
- Machine learning
Background
- High-quality annotated medical data is expensive and time-consuming to acquire.
- Semi-supervised learning reduces annotation burden by using unlabeled data for pseudo-labeling.
- State space models like Mamba excel at capturing long-range dependencies.
Purpose Of The Study
- To explore the potential of advanced state space models in semi-supervised medical image segmentation.
- To propose a novel framework, DCMamba, enhancing diversity across data, network, and features.
Main Methods
- Developed patch-level weak-strong mixing augmentation tailored for Mamba's scanning.
- Introduced a diverse-scan collaboration module to leverage directional prediction discrepancies.
- Implemented uncertainty-weighted contrastive learning to enrich feature representation diversity.
Main Results
- DCMamba demonstrated superior performance in semi-supervised medical image segmentation.
- Achieved a 6.69% improvement over the latest SSM-based method on the Synapse dataset with 20% labeled data.
Conclusions
- DCMamba effectively utilizes data, network, and feature diversity for improved semi-supervised segmentation.
- The proposed framework offers a promising direction for efficient medical image segmentation with limited annotations.
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