Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation

Elena Mulero Ayllón, Linlin Shen, Pierangelo Veltri, Fabrizia Gelardi, Arturo Chiti +2 more
10/31/2025
cs.CVcs.AI

Official code: arco-group/vMambaX (6 stars, MIT, last commit 2025-11-11).

Abstract

Accurate lung tumor segmentation is vital for improving diagnosis and treatment planning, and effectively combining anatomical and functional information from PET and CT remains a major challenge. In this study, we propose vMambaX, a lightweight multimodal framework integrating PET and CT scan images through a Context-Gated Cross-Modal Perception Module (CGM). Built on the Visual Mamba architecture, vMambaX adaptively enhances inter-modality feature interaction, emphasizing informative regions while suppressing noise. Evaluated on the PCLT20K dataset, the model outperforms baseline models while maintaining lower computational complexity. These results highlight the effectiveness of adaptive cross-modal gating for multimodal tumor segmentation and demonstrate the potential of vMambaX as an efficient and scalable framework for advanced lung cancer analysis. The code is available at https://github.com/arco-group/vMambaX.

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Code Implementations(1)

arco-group/vMambaXOfficial100%
MIT

vMambaX: Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation

61C, C++Oct 25, 202511 months agoMIT
attention-mechanismmambamamba-state-space-modelsmultimodalpet-ct+1 more

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Cite this paper

@article{aylln2025contextgated,
  title  = {Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation},
  author = {Elena Mulero Ayllón and Linlin Shen and Pierangelo Veltri and Fabrizia Gelardi and Arturo Chiti and Paolo Soda and Matteo Tortora},
  year   = {2025},
  eprint = {2510.27508},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.27508v1}
}

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