Graph-Based Multi-Modal Light-weight Network for Adaptive Brain Tumor Segmentation

TL;DR

This work proposes GMLN-BTS, a Graph-based Multi-modal interaction Lightweight Network for brain tumor segmentation that reduces parameter count by 98% compared to mainstream 3D Transformers while significantly outperforming existing compact approaches.

Abstract

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.

Figures (3)

• Figure 1: Architectural diagram of the proposed Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor Segmentation (GMLN-BTS)
• Figure 2: Architecture diagram of the proposed VRUM.
• Figure 3: Qualitative visualization results of different models on the BraTS2017 dataset.