Public articles linked to the same research event.
arXiv The work introduces SRMA-Mamba, a Mamba-based network that uses the Spatial Anatomy-Based Mamba (SABMamba) module to perform selective scans across sagittal, coronal, and axial anatomical planes for a global spatial context, and the Spatial Reverse Mamba Attention (SRMA) module to progressively refine boundaries from a coarse segmentation map and hierarchical encoder features, reporting better segmentation metrics than SegResNet, UX-Net, MedNeXt, SwinUNETR, SwinUNETRv2, and SegMamba on CirrMRI600+ T1W and T2W, with fewer parameters and GFLOPs than SegMamba.
The work introduces SRMA-Mamba, a Mamba-based network that uses the Spatial Anatomy-Based Mamba (SABMamba) module to perform selective scans across sagittal, coronal, and axial anatomical planes for a global spatial context, and the Spatial Reverse Mamba Attention (SRMA) module to progressively refine boundaries from a coarse segmentation map and hierarchical encoder features, reporting better segmentation metrics than SegResNet, UX-Net, MedNeXt, SwinUNETR, SwinUNETRv2, and SegMamba on CirrMRI600+ T1W and T2W, with fewer parameters and GFLOPs than SegMamba.
The work introduces SRMA-Mamba, a Mamba-based network that uses the Spatial Anatomy-Based Mamba (SABMamba) module to perform selective scans across sagittal, coronal, and axial anatomical planes for a global spatial context, and the Spatial Reverse Mamba Attention (SRMA) module to progressively refine boundaries from a coarse segmentation map and hierarchical encoder features, reporting better segmentation metrics than SegResNet, UX-Net, MedNeXt, SwinUNETR, SwinUNETRv2, and SegMamba on CirrMRI600+ T1W and T2W, with fewer parameters and GFLOPs than SegMamba.
The work introduces SRMA-Mamba, a Mamba-based network that uses the Spatial Anatomy-Based Mamba (SABMamba) module to perform selective scans across sagittal, coronal, and axial anatomical planes for a global spatial context, and the Spatial Reverse Mamba Attention (SRMA) module to progressively refine boundaries from a coarse segmentation map and hierarchical encoder features, reporting better segmentation metrics than SegResNet, UX-Net, MedNeXt, SwinUNETR, SwinUNETRv2, and SegMamba on CirrMRI600+ T1W and T2W, with fewer parameters and GFLOPs than SegMamba.