# Multi-class Brain Tumor Segmentation using Graph Attention Network

arXiv: https://arxiv.org/abs/2302.05598 · HTML: https://mlsanity.com/paper/2302.05598/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Attention-based Graph Neural Network for Semi-supervised Learning](https://mlsanity.com/paper/1803.03735/) (2018)
- [Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation](https://mlsanity.com/paper/2004.10327/) (2020)
- [A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2109.05580/) (2021)
- [Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks](https://mlsanity.com/paper/2109.01854/) (2021)
- [CxSE: Chest X-ray Slow Encoding CNN forCOVID-19 Diagnosis](https://mlsanity.com/paper/2106.12157/) (2021)

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2505.13584/) (2025)
- [Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks](https://mlsanity.com/paper/2311.11782/) (2023)
- [Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation](https://mlsanity.com/paper/2508.04044/) (2025)
- [3D Graph Attention Networks for High Fidelity Pediatric Glioma Segmentation](https://mlsanity.com/paper/2412.06743/) (2024)

## Abstract (arXiv)

Brain tumor segmentation from magnetic resonance imaging (MRI) plays an important role in diagnostic radiology. To overcome the practical issues in manual approaches, there is a huge demand for building automatic tumor segmentation algorithms. This work introduces an efficient brain tumor summation model by exploiting the advancement in MRI and graph neural networks (GNNs). The model represents the volumetric MRI as a region adjacency graph (RAG) and learns to identify the type of tumors through a graph attention network (GAT) -- a variant of GNNs. The ablation analysis conducted on two benchmark datasets proves that the proposed model can produce competitive results compared to the leading-edge solutions. It achieves mean dice scores of 0.91, 0.86, 0.79, and mean Hausdorff distances in the 95th percentile (HD95) of 5.91, 6.08, and 9.52 mm, respectively, for whole tumor, core tumor, and enhancing tumor segmentation on BraTS2021 validation dataset. On average, these performances are >6% and >50%, compared to a GNN-based baseline model, respectively, on dice score and HD95 evaluation metrics.