A Pixel–Region VSSblock that comprises a local-Mamba branch and a global-Mamba branch to simultaneously capture fine-grained details and high-level semantic context and a Hierarchical Feature Calibration Module (HFCM) that leverages multi-scale context awareness and a structural saliency filtering mechanism to dynamically calibrate the skip connections, effectively suppressing feature redundancy while enhancing semantic interaction quality.
GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.
DIG-MambaNet achieves consistent and competitive performance across diverse target structures and imaging conditions, with improved boundary delineation and favorable overlap-based accuracy compared with representative CNN-, Transformer-, and Mamba-based methods.
Yongkang Zhu, Tianyu Yu, Hongmei Li et al.· Journal of Imaging· 0 citations
Results indicate the WVM-UNet architecture effectively captures discriminative features for precise medical image segmentation, and demonstrates the competitive performance of the method on multiple public datasets.
Yulong Yang, Wenchao Gao, Zheng-Guo Wu et al.· Journal of Imaging· 0 citations
MamNet-PT, a hybrid segmentation architecture that integrates efficient long-range dependency modeling, multi-resolution feature aggregation, and uncertainty-aware prediction within a unified framework, is proposed and suggested to be a promising research framework for accurate and efficient brain tumor segmentation un...
MGA-UNet, a frequency-aware multi-scale encoder–decoder segmentation framework that integrates wavelet-based frequency decomposition with Mamba-based long-range dependency modelling, demonstrates that frequency-domain decomposition and state-space modelling can complement each other for accurate medical image segmentat...
Shuai-Kang Qiu, Xuan Wang, Kai-Le Su et al.· Italian National Conference...· 0 citations
A Parallel Mamba Dual-U Network that adopts a cascaded dual U-Net encoder-decoder for two-stage “coarse-to-fine” segmentation refinement and outperforms state-of-the-art methods on most core metrics, verifying its effectiveness and robustness for complex medical image segmentation.
Shao-Qiang Wang, Linhao Zhang, Guiling Shi et al.· PLoS ONE· 0 citations
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