What does this research mean for the field?

Synergizing Mamba state-space models with evidential deep learning creates a highly efficient medical image segmentation framework that achieves competitive accuracy with significantly lower computational costs than transformer-based architectures while providing well-calibrated uncertainty estimates. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop an efficient medical image segmentation framework that balances accuracy and uncertainty quantification.

July 15, 2026Scientific ReportsOpen Access

Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning

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Authors

SSS Y Sun CLChihui Long XDXingbo Dong

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Overview

Randomized trial shows improved segmentation performance in medical images, suggesting enhanced clinical utility.

Key Points

The aim is to develop an efficient medical image segmentation framework that balances accuracy and uncertainty quantification.
Proposed an uncertainty-aware segmentation framework using Mamba state-space models.
Applied a selective state-space mechanism to capture long-range dependencies.
Conducted experiments on five 2D benchmarks to evaluate performance on segmentation tasks.
Achieved 82.67% mean Dice on Synapse, outperforming Swin-UNet by 1.46%.
Demonstrated 3.5× parameter reduction and 4.8× efficiency gain with only 7.8M parameters and 4.7 GFLOPs.
Achieved real-time inference at 37.2 FPS with an expected calibration error of 0.046.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a57236f88b21df8754802ee https://doi.org/10.1038/s41598-026-62118-w

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July 15, 2026Scientific ReportsOpen Access

Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning

View Full Paper

Ask AI

Bookmark

Authors

SSS Y Sun CLChihui Long XDXingbo Dong

Discussion

Member takes

Overview

Randomized trial shows improved segmentation performance in medical images, suggesting enhanced clinical utility.

Key Points

The aim is to develop an efficient medical image segmentation framework that balances accuracy and uncertainty quantification.
Proposed an uncertainty-aware segmentation framework using Mamba state-space models.
Applied a selective state-space mechanism to capture long-range dependencies.
Conducted experiments on five 2D benchmarks to evaluate performance on segmentation tasks.
Achieved 82.67% mean Dice on Synapse, outperforming Swin-UNet by 1.46%.
Demonstrated 3.5× parameter reduction and 4.8× efficiency gain with only 7.8M parameters and 4.7 GFLOPs.
Achieved real-time inference at 37.2 FPS with an expected calibration error of 0.046.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a57236f88b21df8754802ee https://doi.org/10.1038/s41598-026-62118-w

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