# EfficientGFormer: Multimodal Brain Tumor Segmentation via Pruned Graph-Augmented Transformer

arXiv: https://arxiv.org/abs/2508.01465 · HTML: https://mlsanity.com/paper/2508.01465/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion](https://mlsanity.com/paper/2002.09708/) (2020)
- [A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos](https://mlsanity.com/paper/2310.00670/) (2023)

## Built on by (most cited first, 5 of 5)

- [Neuro-Symbolic Manipulation Understanding with Enriched Semantic Event Chains](https://mlsanity.com/paper/2604.21053/) (2026)
- [Why Relational Graphs Will Save the Next Generation of Vision Foundation Models?](https://mlsanity.com/paper/2508.18421/) (2025)
- [Trustworthy Visual Predicates for Robust Manipulation Understanding under Degradation](https://mlsanity.com/paper/2606.08121/) (2026)
- [Beyond Pairwise Relations: Dynamic Manipulation Hypergraphs for Vision-Based Human Activity Recognition](https://mlsanity.com/paper/2607.14350/) (2026)
- [Lag-aware cross-hand alignment for dual-hand action segmentation](https://mlsanity.com/paper/2607.26215/) (2026)

## Abstract (arXiv)

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.