Multi-Head Mamba Enhances 3D Brain Tumor Segmentation
A novel approach known as Multi-Head Mamba (MHMamba) has been introduced for segmenting 3D brain tumors from MRI images. Due to the significant heterogeneity of brain tumors, manual delineation can be quite labor-intensive. MHMamba integrates a U-shaped architecture with a multi-head state-space model (Mamba), dividing channel dimensions into parallel SSM heads that are combined with residuals. This method enhances long-range representation and stability in multimodal training while preserving linear complexity. Additionally, a channel-space calibration module and an adaptive fusion mechanism further enhance the response to lesions. This technique overcomes the challenges faced by CNNs regarding long-range dependencies and mitigates the computational demands of Transformers in 3D MRI analysis.
Key facts
- MHMamba combines U-shaped architecture with multi-head state-space model
- Splits channel dimension into parallel SSM heads with residual aggregation
- Enhances long-range representation and multimodal training stability
- Maintains linear complexity
- Includes channel-space calibration module for multi-head outputs
- Introduces adaptive fusion mechanism
- Addresses CNN limitations in long-range dependencies
- Addresses Transformer computational overhead in 3D MRI
Entities
Institutions
- arXiv