Efficient Brain Tumor Segmentation with Mamba

This research investigates the application of Structured State Space Sequence Models (SSMs), particularly the Mamba architecture, for brain tumor segmentation in MRI scans. The Mamba model uses selective state spaces to demonstrate efficient and effective segmentation while maintaining linear scalability. Comparative experiments with state-of-the-art models reveal compelling performance in both model size and segmentation accuracy. Our findings indicate the potential of Mamba-based approaches to surpass transformer-based models for this critical medical imaging task, offering a promising alternative in resource-constrained environments. Further refinements and optimizations of Mamba-based models, along with exploration of interpretability and robustness, present avenues for future research and clinical deployment.