Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation
This work addresses lung tumor segmentation for improved cancer diagnosis and treatment planning, presenting an incremental improvement through adaptive cross-modal gating.
The study tackled the challenge of accurately segmenting lung tumors by combining PET and CT scan information, proposing vMambaX, a lightweight multimodal framework that outperformed baseline models on the PCLT20K dataset while maintaining lower computational complexity.
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.