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

Elena Mulero Ayllon; Linlin Shen; Pierangelo Veltri; Fabrizia Gelardi; Arturo Chiti; Paolo Soda; Matteo Tortora

doi:10.1109/ISBI61048.2026.11515799

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

Elena Mulero Ayllon
, Linlin Shen
, Pierangelo Veltri
, Fabrizia Gelardi
, Arturo Chiti
, Paolo Soda
, Matteo Tortora^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
ISBN (Electronic)	9798331577636
DOIs	https://doi.org/10.1109/ISBI61048.2026.11515799
Publication status	Published - 2026
Externally published	Yes
Event	23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026 - London, United Kingdom Duration: 8 Apr 2026 → 11 Apr 2026

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2026-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Country/Territory	United Kingdom
City	London
Period	8/04/26 → 11/04/26

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Free Keywords

Lung Cancer
Mamba
Multimodal Fusion
PET-CT Segmentation

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI61048.2026.11515799

Cite this

Ayllon, E. M., Shen, L., Veltri, P., Gelardi, F., Chiti, A., Soda, P., & Tortora, M. (2026). Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation. In ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging (Proceedings - International Symposium on Biomedical Imaging; Vol. 2026-April). IEEE Computer Society. https://doi.org/10.1109/ISBI61048.2026.11515799

@inproceedings{28636999fc174c65adae57eba216a7cf,

title = "Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation",

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.",

keywords = "Lung Cancer, Mamba, Multimodal Fusion, PET-CT Segmentation",

author = "Ayllon, \{Elena Mulero\} and Linlin Shen and Pierangelo Veltri and Fabrizia Gelardi and Arturo Chiti and Paolo Soda and Matteo Tortora",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026 ; Conference date: 08-04-2026 Through 11-04-2026",

year = "2026",

doi = "10.1109/ISBI61048.2026.11515799",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging",

address = "United States",

}

Ayllon, EM, Shen, L, Veltri, P, Gelardi, F, Chiti, A, Soda, P & Tortora, M 2026, Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation. in ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging. Proceedings - International Symposium on Biomedical Imaging, vol. 2026-April, IEEE Computer Society, 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026, London, United Kingdom, 8/04/26. https://doi.org/10.1109/ISBI61048.2026.11515799

Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation. / Ayllon, Elena Mulero; Shen, Linlin; Veltri, Pierangelo et al.
ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2026. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2026-April).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation

AU - Ayllon, Elena Mulero

AU - Shen, Linlin

AU - Veltri, Pierangelo

AU - Gelardi, Fabrizia

AU - Chiti, Arturo

AU - Soda, Paolo

AU - Tortora, Matteo

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Lung Cancer

KW - Mamba

KW - Multimodal Fusion

KW - PET-CT Segmentation

UR - https://www.scopus.com/pages/publications/105041597427

U2 - 10.1109/ISBI61048.2026.11515799

DO - 10.1109/ISBI61048.2026.11515799

M3 - Conference contribution

AN - SCOPUS:105041597427

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026

Y2 - 8 April 2026 through 11 April 2026

ER -

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

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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Cite this