Multi-modal brain tumor segmentation remains challenging for practical deployment due to the high computational costs of mainstream models. In this work, we propose GMLN-BTS, a Graph-based Multi-modal interaction Lightweight Network for brain tumor segmentation. Our architecture achieves high-precision, resource-efficient segmentation through three key components. First, a Modality-Aware Adaptive Encoder (M2AE) facilitates efficient multi-scale semantic extraction. Second, a Graph-based Multi-Modal Collaborative Interaction Module (G2MCIM) leverages graph structures to model complementary cross-modal relationships. Finally, a Voxel Refinement UpSampling Module (VRUM) integrates linear interpolation with multi-scale transposed convolutions to suppress artifacts and preserve boundary details. Experimental results on BraTS 2017, 2019, and 2021 benchmarks demonstrate that GMLN-BTS achieves state-of-the-art performance among lightweight models. With only 4.58M parameters, our method reduces parameter count by 98% compared to mainstream 3D Transformers while significantly outperforming existing compact approaches.
@article{arxiv.2507.09995,
title = {Graph-Based Multi-Modal Light-weight Network for Adaptive Brain Tumor Segmentation},
author = {Guohao Huo and Ruiting Dai and Zitong Wang and Junxin Kong and Hao Tang},
journal= {arXiv preprint arXiv:2507.09995},
year = {2026}
}