Mamba UNet

Mamba UNet represents a family of U-Net-based architectures leveraging State Space Models (SSMs), specifically the Mamba algorithm, for medical and remote sensing image segmentation. Research focuses on improving Mamba UNet's efficiency and accuracy through hybrid approaches combining it with Convolutional Neural Networks (CNNs) and Transformers, exploring variations in Mamba's architecture (e.g., large kernels, parallel processing), and incorporating attention mechanisms. These advancements aim to address limitations of traditional CNNs and Transformers in handling long-range dependencies and computational complexity, ultimately leading to more accurate and efficient image segmentation in various applications.