Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
<|reference_start|>Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation: Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis, multi-modal brain tumor segmentation aims to delineate the malignant entity involving multiple modalities. Although existing methods have shown remarkable performance in the task, the information exchange for cross-scale and high-level representations fusion in spatial and modality are limited in these methods. In this paper, we present a novel Modality Aware and Shift Mixer that integrates intra-modality and inter-modality dependencies of multi-modal images for effective and robust brain tumor segmentation. Specifically, we introduce a Modality-Aware module according to neuroimaging studies for modeling the specific modality pair relationships at low levels, and a Modality-Shift module with specific mosaic patterns is developed to explore the complex relationships across modalities at high levels via the self-attention. Experimentally, we outperform previous state-of-the-art approaches on the public Brain Tumor Segmentation (BraTS 2021 segmentation) dataset. Further qualitative experiments demonstrate the efficacy and robustness of MASM.<|reference_end|>
@article{huang2024modality-aware,
    title={Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation},
    author={Zhongzhen Huang, Linda Wei, Shaoting Zhang, Xiaofan Zhang},
    journal={arXiv preprint arXiv:2403.02074},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02074},
    primaryClass={cs.CV cs.AI}
}