Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning.
TL;DR
This work proposes an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning, employing a 2D-adapted selective state-space mechanism to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling.
Abstract
Medical image segmentation requires balancing accuracy, computational efficiency, and uncertainty quantification for potential clinical deployment. Transformer-based architectures achieve superior performance through global context modeling but demand prohibitive computational resources (>20 GFLOPs), while lightweight convolutional networks sacrifice accuracy due to limited receptive fields. We propose an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning. Our method employs a 2D-adapted selective state-space mechanism (cross-scan over four directions) to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling. The uncertainty-guided attention module (UGAM) leverages Dirichlet-parameterized evidential learning to decompose epistemic and aleatoric uncertainty, adaptively recalibrating features through spatial-channel attention conditioned on prediction confidence. Progressive multi-scale fusion with gradient-based uncertainty supervision enhances boundary delineation and calibration. Experiments on five 2D benchmarks show competitive performance: 82.67% mean Dice on Synapse (1.46% improvement over Swin-UNet, and competitive with state-space peers U-Mamba and Swin-UMamba retrained under the same 2D protocol) with only 7.8M parameters and 4.7 GFLOPs-representing [Formula: see text] parameter reduction and [Formula: see text] efficiency gain. Real-time inference at 37.2 FPS with well-calibrated uncertainty (Expected Calibration Error: 0.046) supports further evaluation of its potential value for time-sensitive clinical workflow analysis rather than direct clinical deployment. A small, single-center reader study using 150 ACDC cases and three radiologists suggested that uncertainty visualization may be associated with improved reader confidence (22.2%) and reduced decision time (18.6%); these exploratory findings require prospective, multi-reader, multi-scanner validation. We explicitly do not claim generalization to 3D volumes, high-resolution pathology, multi-phase CT/MRI, or severe class-imbalance regimes, which are left to future work.