HGN-GT: Hierarchical Graph Neural Network and Graph Transformer for Brain Tumor MRI Classification

Project Core Information

• Project Name: HGN-GT (Hierarchical Graph Neural Network with Graph Transformer)
• Goal: Brain tumor MRI image classification
• Core Technologies: Hierarchical graph neural network + Graph Transformer, supporting dynamic edge learning, multi-scale graph convolution, and hierarchical feature fusion
• Implementation Framework: PyTorch
• Source: GitHub (Author: adnansaeed149, Release Date: June 5, 2026, Link: https://github.com/adnansaeed149/HGN-GT)

Core View

HGN-GT innovatively combines the advantages of Graph Neural Networks (GNN) and Transformer architectures. Through hierarchical feature extraction and fusion, it solves the modeling problem of irregular lesions in medical images, providing an accurate and automated solution for brain tumor classification.

Clinical Needs

Early accurate diagnosis of brain tumors is crucial for treatment and prognosis. MRI is the main diagnostic tool, but manual interpretation relies on professional experience and has subjective differences.

Common Brain Tumor Types

• Glioma: Originates from glial cells, divided into low/high grades (e.g., glioblastoma is highly malignant)
• Meningioma: Originates from the meninges, usually benign, slow-growing with clear boundaries
• Pituitary Tumor: Originates from the pituitary gland, may affect hormone secretion, most are benign

Advantages of GNN in Medical Imaging

1. Non-Euclidean Structure Modeling: Flexibly represents irregular lesion areas and models complex spatial relationships between pixels
2. Interpretability: Visualizes attention weights to understand the regions the model focuses on, providing a basis for clinical decision-making
3. Data Efficiency: Uses graph topology priors and parameter sharing, leading to stronger generalization ability with limited data

1. Hierarchical Graph Neural Network Architecture

Simulates the process of radiologists reading images:

• Low Layer: Extracts local detailed textures
• Middle Layer: Aggregates regional information and identifies local lesion patterns
• High Layer: Performs global reasoning to determine tumor types

2. Dynamic Edge Learning

• Does not predefine fixed graph structures; adaptively learns node connections based on feature similarity
• Focuses on relevant regions to improve modeling ability for complex lesions

3. Multi-scale Graph Convolution

Combines three methods:

• GATv2: Uses attention mechanism to assign neighbor weights and dynamically focus on important features
• GCN: Classic spectral domain method for efficient neighbor information aggregation
• GraphSAGE: Inductive learning to handle unseen nodes and enhance generalization

4. Graph Transformer Module

• Self-attention captures global dependencies and models long-distance node interactions
• Compensates for the deficiency of pure GNNs in modeling long-range dependencies

5. Hierarchical Feature Fusion

• Combines low-level details and high-level semantic features
• Uses auxiliary classifiers to supervise intermediate layer training, alleviating gradient vanishing and improving performance

PyTorch Architecture Components

• Graph Construction Module: Converts MRI images into graph representations
• Encoder Network: Extracts initial node features
• Graph Convolution Layer: Implements GATv2/GCN/GraphSAGE
• Transformer Layer: Computes graph-level self-attention
• Fusion Module: Integrates multi-scale features
• Classification Head: Predicts tumor types

Training Strategy

• Loss Function: Cross-entropy loss + Auxiliary classifier loss
• Optimizer: Adam/AdamW
• Learning Rate Scheduling: Cosine annealing / Piecewise decay
• Regularization: Dropout, weight decay to prevent overfitting
• Data Augmentation: Rotation, flipping, intensity transformation, etc.

Project Value

1. Technical Innovation: A cutting-edge example of combining GNN and Transformer in medical applications, providing reference for other imaging tasks
2. Clinical Potential: Assists doctors in improving diagnostic efficiency, reducing subjective differences, providing objective basis for treatment, and supporting resource-poor areas
3. Open-source Contribution: Provides reproducible benchmarks, modular code, and application examples

Current Limitations

• Relies on large amounts of labeled data
• Generalization ability across different devices/scanning parameters needs verification
• Interpretability still faces challenges (black-box characteristics)
• High computational complexity

Improvement Directions

• Multi-modal fusion (combining T1/T2/FLAIR and other MRI sequences)
• Semi-supervised/federated learning (using unlabeled data or multi-center privacy data)
• Lightweight design (adapting to edge devices)
• Uncertainty quantification (predicting confidence to assist decision-making)

HGN-GT represents an important direction of medical imaging AI: applying advanced GNN and Transformer to clinical problems. Although the documentation is concise, its architecture design reflects an in-depth understanding of brain tumor classification.

With the progress of deep learning and accumulation of medical data, AI-assisted diagnosis will play a greater role in neurosurgery and radiology. As an open-source project, HGN-GT provides valuable technical solutions and practical experience for the development of the field.

For researchers exploring the application of GNN in medical imaging, this is a project worth paying attention to and learning from.