EfficientGFormer: Multimodal Brain Tumor Segmentation via Pruned Graph-Augmented Transformer
This provides a clinically viable solution for fast and accurate volumetric tumor delineation in neuroimaging, though it appears incremental as it builds on existing transformer and graph methods.
The paper tackled brain tumor segmentation from MRI by proposing EfficientGFormer, which integrates pretrained models with graph reasoning and pruning, achieving state-of-the-art accuracy with reduced memory and inference time on MSD Task01 and BraTS 2021 datasets.
Accurate and efficient brain tumor segmentation remains a critical challenge in neuroimaging due to the heterogeneous nature of tumor subregions and the high computational cost of volumetric inference. In this paper, we propose EfficientGFormer, a novel architecture that integrates pretrained foundation models with graph-based reasoning and lightweight efficiency mechanisms for robust 3D brain tumor segmentation. Our framework leverages nnFormer as a modality-aware encoder, transforming multi-modal MRI volumes into patch-level embeddings. These features are structured into a dual-edge graph that captures both spatial adjacency and semantic similarity. A pruned, edge-type-aware Graph Attention Network (GAT) enables efficient relational reasoning across tumor subregions, while a distillation module transfers knowledge from a full-capacity teacher to a compact student model for real-time deployment. Experiments on the MSD Task01 and BraTS 2021 datasets demonstrate that EfficientGFormer achieves state-of-the-art accuracy with significantly reduced memory and inference time, outperforming recent transformer-based and graph-based baselines. This work offers a clinically viable solution for fast and accurate volumetric tumor delineation, combining scalability, interpretability, and generalization.