标题： EfficientGFormer：通过剪枝图增强变压器进行多模态脑肿瘤分割 显示英文标题

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

摘要： 准确且高效的脑肿瘤分割在神经影像学中仍然是一个关键挑战，这是由于肿瘤亚区域的异质性以及体积推理的高计算成本。 在本文中，我们提出了 EfficientGFormer，一种新颖的架构，该架构将预训练基础模型与基于图的推理和轻量级效率机制相结合，以实现稳健的三维脑肿瘤分割。 我们的框架利用nnFormer作为模态感知编码器，将多模态MRI体积转换为补丁级嵌入。 这些特征被结构化为一个双边图，捕捉空间邻接性和语义相似性。 一个剪枝的、边类型感知的图注意力网络（GAT）实现了跨肿瘤亚区域的有效关系推理，同时一个蒸馏模块将知识从全容量教师模型转移到紧凑的学生模型中，以便实时部署。 在MSD Task01和BraTS 2021数据集上的实验表明， EfficientGFormer在显著减少内存和推理时间的情况下实现了最先进的准确性，优于最近的基于变压器和基于图的基线方法。 这项工作为快速且准确的体积肿瘤勾画提供了一个临床上可行的解决方案，结合了可扩展性、可解释性和泛化能力。 显示英文摘要

摘要： 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.