Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
Haifan Gong, Luoyao Kang, Yitao Wang, Yihan Wang, Xiang Wan, Xusheng Wu, Haofeng Li
Abstract
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 to enhance performance in dense prediction and classification tasks. Extensive experiments on seven datasets demonstrate that nnMamba outperforms current state-of-the-art methods in 3D image segmentation, classification, and landmark detection. nnMamba effectively integrates CNNs' local representation with SSMs' global con-text processing, establishing a new benchmark for long-range dependency modeling in medical image analysis. Code is available at https://github.com/lhaof/nnMamba.