MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2605.16464v1
• Date: Fri, 15 May 2026 10:05:30 GMT
• Title: MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
• Abstract summary: This paper proposes Multi-Head Mamba (MHMamba) to address the limitations of CNNs in modeling long-range dependencies.<n>Method combines a U-shaped architecture with a multi-head state-space model (Mamba), splitting the channel dimension into parallel SSM heads and aggregating them with residuals.<n>Results: MHMamba achieved stable and significant improvements in overall accuracy, boundary smoothness, and sensitivity to tumor core and small-volume enhancement areas.
• Score: 6.1337977581640075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience-dependent, thus necessitating efficient and stable automated segmentation methods. To address the limitations of CNNs in modeling long-range dependencies, and the heavy computational and memory overhead and inter-block contextual in coherence of Transformers in 3D MRI, this paper proposes Multi-Head Mamba (MHMamba). This method combines a U-shaped architecture with a multi-head state-space model (Mamba), splitting the channel dimension into parallel SSM heads and aggregating them with residuals. This enhances long-range representation and improves the stability of multimodal training while maintaining linear complexity. To further align statistics and enhance lesion response, we designed a channel-space calibration module for multi-head outputs and introduced an adaptive fusion mechanism at skip connections to dynamically connect global semantics with local details, thereby improving boundary consistency and the detection of small-volume lesions. We conducted experiments and ablations on BraTS2021 and BraTS2023. The results showed that MHMamba achieved stable and significant improvements in overall accuracy, boundary smoothness, and sensitivity to tumor core and small-volume enhancement areas, while preserving the linear-complexity advantage of Mamba-based modeling, thus verifying the effectiveness and versatility of the method.

Related papers

• AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis [57.942670844512016]
  We introduce AdaSurvMamba as a novel adaptive framework for multimodal survival analysis.<n>The framework features a Dual-Scale Importance-Aware Reconstruction (DSIR) module to dynamically modulate cross-modal interaction strength.<n>Experiments across five TCGA cohorts demonstrate consistent gains over existing methods.
  arXiv  Detail & Related papers  (2026-06-28T11:36:31Z)
• MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection [108.36437360254605]
  This paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection.<n>MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales.<n>The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling.
  arXiv  Detail & Related papers  (2026-06-22T10:14:06Z)
• BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation [6.344197178127241]
  We propose BiSegMamba, an efficient tri-oriented Mamba network for 3D medical image segmentation.<n>BiSegMamba generalizes well across vascular, cardiac, brain tumor, and abdominal multi-organ segmentation tasks.<n>Compared with SegMamba-V2, BiSegMamba achieves slightly better performance on BraTS2023 and clear improvements on ACDC and the carotid dataset.
  arXiv  Detail & Related papers  (2026-05-29T08:13:22Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation [10.906979279002577]
  NeuroMamba is a multi-perspective framework that exploits the linear complexity of Mamba to enable patch-free global modeling.<n>Our method demonstrates state-of-the-art performance across four public EM datasets, validating its exceptional adaptability to both anisotropic and isotropic resolutions.
  arXiv  Detail & Related papers  (2026-01-22T13:06:24Z)
• TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition [59.99922360648663]
  TSkel-Mamba is a hybrid Transformer-Mamba framework that effectively captures both spatial and temporal dynamics.<n>The MTI module employs multi-scale Cycle operators to capture cross-channel temporal interactions, a critical factor in action recognition.
  arXiv  Detail & Related papers  (2025-12-12T11:55:16Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy [15.270952880303533]
  We propose a novel 3D multi-modal image segmentation framework, MSD-KMamba.<n>It integrates bidirectional spatial perception with multi-scale self-distillation.<n>Our framework consistently outperforms state-of-the-art methods in segmentation accuracy, robustness, and generalization.
  arXiv  Detail & Related papers  (2025-09-28T06:34:01Z)
• Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection [88.47928738482719]
  Linear State Space Models (SSMs) offer remarkable performance gains in sequence modeling.<n>Recent advances, such as Mamba, further enhance SSMs with input-dependent gating and hardware-aware implementations.<n>We introduce Routing Mamba (RoM), a novel approach that scales SSM parameters using sparse mixtures of linear projection experts.
  arXiv  Detail & Related papers  (2025-06-22T19:26:55Z)
• Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation [41.14182025718559]
  We propose the Mamba-Driven Topology Fusion framework for 3D human pose estimation.<n>Specifically, the proposed Bone Aware Module infers the direction and length of bone vectors in the spherical coordinate system.<n>We also design a Spatiotemporal Refinement Module to model both temporal and spatial relationships within the sequence.
  arXiv  Detail & Related papers  (2025-05-27T01:21:57Z)
• MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation [6.578088710294546]
  Traditional segmentation methods struggle to address challenges such as high anatomical variability, blurred tissue boundaries, low organ contrast, and noise. We propose MLLA-UNet (Mamba-Like Linear Attention UNet), a novel architecture that achieves linear computational complexity while maintaining high segmentation accuracy. Experiments demonstrate that MLLA-UNet achieves state-of-the-art performance on six challenging datasets with 24 different segmentation tasks, including but not limited to FLARE22, AMOS CT, and ACDC, with an average DSC of 88.32%.
  arXiv  Detail & Related papers  (2024-10-31T08:54:23Z)
• MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection [53.03687787922032]
  Mamba-based models with superior long-range modeling and linear efficiency have garnered substantial attention.<n>This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD.<n>The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi- kernel convolutions operations, effectively captures both long-range and local information.
  arXiv  Detail & Related papers  (2024-04-09T18:28:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.