Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

In biomedical image analysis, developing architectures that effectively capture long-range dependencies is crucial. Traditional Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers, though proficient in global context integration, are computationally demanding for high-dimensional medical images. Here, we present nnMamba, a novel architecture that combines the strengths of CNNs with the long-range modeling capabilities of State Space Models (SSMs). We introduce the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model long-range voxel relationships. Additionally, we implement channel scaling and channel-sequential learning methods…