Is Mamba Reliable for Medical Imaging?
On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis
Abstract (arXiv)
State-space models like Mamba offer linear-time sequence processing and low memory, making them attractive for medical imaging. However, their robustness under realistic software and hardware threat models remains underexplored. This paper evaluates Mamba on multiple MedM-NIST classification benchmarks under input-level attacks, including white-box adversarial perturbations (FGSM/PGD), occlusion-based PatchDrop, and common acquisition corruptions (Gaussian noise and defocus blur) as well as hardware-inspired fault attacks emulated in software via targeted and random bit-flip injections into weights and activations. We profile vulnerabilities and quantify impacts on accuracy indicating that defenses are needed for deployment.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 14 of its 19 references that are in the map. First posted on arXiv 16 Feb 2026.
Builds on
14 of 19 references.
Closest ancestors · 8
Same direction, newest first
- 2024A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond3 Oct
- 2024On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models12 Jun
- 2024H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation20 Mar
- 2024MedMamba: Vision Mamba for Medical Image Classification6 Mar
- 2024MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation28 Feb
- 2024FD-Vision Mamba for Endoscopic Exposure Correction9 Feb
- 2024MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration25 Jan
- 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation24 Jan
Other references · 6
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
- 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
- 2022Adaptive-Gravity: A Defense Against Adversarial Samplescited by 2
- 2025Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imagingcited by 1
- 2025FaRAccel: FPGA-Accelerated Defense Architecture for Efficient Bit-Flip Attack Resilience in Transformer Modelscited by 1
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Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025MedMambaLite: Hardware-Aware Mamba for Medical Image Classification
- 2025COBRA: Catastrophic Bit-flip Reliability Analysis of State-Space Models
- 2024Understanding Robustness of Visual State Space Models for Image Classification
- 2024Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
- 2026Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
- 2026Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
- 2025BadViM: Backdoor Attack against Vision Mamba
- 2025SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection