MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis

• URL: http://arxiv.org/abs/2511.12193v1
• Date: Sat, 15 Nov 2025 12:57:25 GMT
• Title: MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis
• Authors: Abdelrahman Elsayed, Ahmed Jaheen, Mohammad Yaqub,
• Abstract summary: MMRINet is a lightweight architecture that replaces quadratic-complexity attention with linear-complexity Mamba state-space models.<n>In the BraTS-Lighthouse SSA 2025, our model achieves strong volumetric performance with an average Dice score of (0.752) and an average HD95 of (12.23) with only 2.5M parameters.
• Score: 2.6992900249585765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated brain tumor segmentation in multi-parametric MRI remains challenging in resource-constrained settings where deep 3D networks are computationally prohibitive. We propose MMRINet, a lightweight architecture that replaces quadratic-complexity attention with linear-complexity Mamba state-space models for efficient volumetric context modeling. Novel Dual-Path Feature Refinement (DPFR) modules maximize feature diversity without additional data requirements, while Progressive Feature Aggregation (PFA) enables effective multi-scale fusion. In the BraTS-Lighthouse SSA 2025, our model achieves strong performance with an average Dice score of (0.752) and an average HD95 of (12.23) with only ~2.5M parameters, demonstrating efficient and accurate segmentation suitable for low-resource clinical environments. Our GitHub repository can be accessed here: github.com/BioMedIA-MBZUAI/MMRINet.

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