[2603.22002] SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.22002 (cs) [Submitted on 23 Mar 2026] Title:SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation Authors:Duy D. Nguyen, Phat T. Tran-Truong View a PDF of the paper titled SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation, by Duy D. Nguyen and Phat T. Tran-Truong View PDF HTML (experimental) Abstract:The advent of Transformer and Mamba-based architectures has significantly advanced 3D medical image segmentation by enabling global contextual modeling, a capability traditionally limited in Convolutional Neural Networks (CNNs). However, state-of-the-art Transformer models often entail substantial computational complexity and parameter counts, which is particularly prohibitive for volumetric data and further exacerbated by the limited availability of annotated medical imaging datasets. To address these limitations, this work introduces SegMaFormer, a lightweight hybrid architecture that synergizes Mamba and Transformer modules within a hierarchical volumetric encoder for efficient long-range dependency modeling. The model strategically employs Mamba-based layers in early, high-resolution stages to reduce computational overhead while capturing essential spatial context, and reserves self-attention mechanisms for later, lower-resolution stages to refine feature representation. This design is augmented with generalized rotary position embeddings to e...