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arxiv: 2605.17719 · v1 · pith:F2TYA3ODnew · submitted 2026-05-18 · 💻 cs.CV

Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation

Diego Adame , Fabian Vazquez , Jose A. Nunez , Huimin Li , Jinghao Yang , Erik Enriquez , DongChul Kim , Haoteng Tang
show 2 more authors
Bin Fu Pengfei Gu
This is my paper

Pith reviewed 2026-05-19 21:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords medical image segmentationstate space modelsMambamixture of expertspatch-ordered scanningpolyp segmentationskin lesion segmentation
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The pith

Patch-MoE Mamba addresses limitations in Mamba models by using hierarchical patch-ordered scanning and mixture-of-experts fusion for medical image segmentation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to improve medical image segmentation using state space models by fixing two issues in current Mamba-based approaches. Pixel-wise scanning disrupts local spatial structures in 2D images, and fixed summation of different scan directions fails to handle varying object shapes well. The proposed Patch-MoE Mamba uses a hierarchical patch-ordered scanning to keep local neighborhoods intact while getting multi-scale context, and an MoE module that adaptively fuses outputs from directional experts. This is tested on polyp and skin lesion datasets to show better results with efficient computation.

Core claim

The central discovery is that a patch-ordered mixture-of-experts state space architecture can effectively model long-range dependencies in medical images while preserving local 2D spatial structure through hierarchical scanning and adaptive directional fusion using four directional experts, a learnable concatenation expert, and residual aggregation.

What carries the argument

Hierarchical patch-ordered scanning mechanism that processes image patches in an ordered way to maintain spatial neighborhoods, combined with MoE-based directional fusion module that adaptively combines multiple Mamba scanner outputs.

If this is right

  • Better performance on polyp segmentation benchmarks by preserving local structure.
  • Improved adaptability to diverse object sizes and boundaries in skin lesion segmentation.
  • Linear sequence complexity maintained while capturing multi-scale context.
  • Generality demonstrated across five polyp datasets and ISIC skin lesion datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This approach might allow state space models to be applied more broadly in computer vision tasks requiring spatial awareness.
  • Future work could explore extending the patch ordering to 3D medical volumes for volumetric segmentation.
  • The MoE fusion could be applied to other multi-directional scanning problems in sequence modeling.

Load-bearing premise

The hierarchical patch-ordered scanning mechanism preserves local spatial neighborhoods while capturing multi-scale context better than standard pixel-wise directional scanning.

What would settle it

Running the model on the same five polyp benchmarks and ISIC datasets and finding no improvement in standard metrics such as Dice coefficient or IoU compared to prior Mamba models would falsify the effectiveness claim.

Figures

Figures reproduced from arXiv: 2605.17719 by Bin Fu, Diego Adame, Dongchul Kim, Erik Enriquez, Fabian Vazquez, Haoteng Tang, Huimin Li, Jinghao Yang, Jose A. Nunez, Pengfei Gu.

Figure 1
Figure 1. Figure 1: (a) Overview of the proposed Patch-MoE Mamba architecture. (b) Structure of the Patch-MoE Visual State Space (VSS) block. (c) Structure of the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the patch-ordered scanning method on a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed MoE-based directional fusion module. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visual examples of segmentations results. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformers often suffer from quadratic computational and memory complexity. State space models, especially Mamba-based networks, offer an efficient alternative with linear sequence complexity. However, existing Mamba segmentation models still face two limitations: pixel-wise directional scanning can disrupt local 2D spatial structure, and simple summation-based fusion of scan directions cannot adapt well to diverse object sizes, shapes, and boundaries. To address these issues, we propose \textit{Patch-MoE Mamba}, a patch-ordered mixture-of-experts state space architecture for medical image segmentation. It introduces a hierarchical patch-ordered scanning mechanism that preserves local spatial neighborhoods while capturing multi-scale context, and an MoE-based directional fusion module that adaptively combines multiple Mamba scanner outputs using four directional experts, a learnable concatenation expert, and residual directional aggregation. Experiments on five public polyp segmentation benchmarks and the ISIC 2017/2018 skin lesion segmentation datasets demonstrate the effectiveness and generality of Patch-MoE Mamba.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes Patch-MoE Mamba, a Mamba-based architecture for medical image segmentation. It introduces a hierarchical patch-ordered scanning mechanism intended to preserve local 2D spatial neighborhoods while capturing multi-scale context, and an MoE-based directional fusion module that adaptively combines outputs from four directional experts, a learnable concatenation expert, and residual aggregation. The central claim is that these components address limitations of pixel-wise scanning and non-adaptive fusion in prior Mamba segmentation models, with effectiveness demonstrated on five public polyp segmentation benchmarks and the ISIC 2017/2018 skin lesion datasets.

Significance. If the reported gains hold under rigorous controls, the work offers a concrete advance in efficient (linear-complexity) segmentation models for medical imaging by directly targeting the locality disruption and fusion rigidity of existing Mamba approaches. Evaluation across multiple public benchmarks supports generality claims and enables direct comparison; the explicit architectural choices (patch ordering, expert routing) are falsifiable and could be adopted or extended by others.

minor comments (3)
  1. [Abstract] Abstract: quantitative results (Dice, IoU, etc.), exact baselines, and statistical significance are referenced only qualitatively; adding one sentence with key numbers would strengthen the summary.
  2. [§3.2] The hierarchical patch-ordered scanning is described at a high level; a small diagram or pseudocode in §3.2 would clarify how patch ordering differs from standard directional scans while preserving neighborhoods.
  3. [Experiments] Table captions and axis labels in the experimental section should explicitly state the evaluation metric (e.g., mean Dice) and whether results are averaged over multiple runs.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive evaluation of our work and the recommendation for minor revision. We appreciate the acknowledgment that the hierarchical patch-ordered scanning and MoE-based directional fusion target key limitations in prior Mamba segmentation models, with evaluation on multiple public benchmarks supporting the claims.

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper defines its Patch-MoE Mamba architecture independently by introducing a hierarchical patch-ordered scanning mechanism to preserve local 2D neighborhoods and an MoE-based directional fusion module with four directional experts plus learnable concatenation. These components directly address the stated limitations of pixel-wise scanning and non-adaptive fusion in prior Mamba models. The central effectiveness claim rests on experimental validation across five external public polyp segmentation benchmarks and ISIC 2017/2018 datasets, with no reduction of predictions to fitted inputs, no load-bearing self-citations, and no self-definitional loops in the architecture equations or motivation. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The proposal rests on standard deep-learning assumptions about spatial structure in images and the benefits of adaptive fusion; no new physical entities or ungrounded constants are introduced.

free parameters (2)
  • Number of directional experts
    Fixed at four in the MoE module description.
  • MoE routing and aggregation parameters
    Learnable parameters for concatenation expert and residual directional aggregation.
axioms (2)
  • standard math Mamba state space models provide linear sequence complexity for long-range modeling
    Invoked when contrasting with quadratic Transformer complexity.
  • domain assumption Pixel-wise directional scanning disrupts local 2D spatial structure
    Stated as a core limitation of existing Mamba segmentation models.

pith-pipeline@v0.9.0 · 5762 in / 1274 out tokens · 39715 ms · 2026-05-19T21:51:16.685165+00:00 · methodology

discussion (0)

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Reference graph

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