Graph Diffusion Learning for Multi-Modal Brain Prediction
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Solution Overview
Problem
Conventional brain disease prediction methods, particularly for neurodegenerative diseases like Alzheimer's, suffer from poor prediction effects due to universal feature redundancy or unique feature damage in multi-modal fusion, leading to a high dependence on professional expertise and time-consuming manual processes.
Innovation Solution
A multi-modal brain network calculation method involving a feature extraction network, graph representation diffusion learning network, and brain network reconstruction network, which separates and aligns structural and functional features using graph self-attention mechanisms and adaptive fusion, followed by a brain network boundary-aware module for prediction, utilizing loss functions for parameter updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multi-modal fusion combines universal features and unique features together with weighted fusion, then the fusion process is simplified, but it causes universal feature redundancy or unique feature damage resulting in poor fusion effect
Solution Approach 1:
The patent segments the fusion process into distinct stages: first fusing universal features separately, then fusing unique features separately, and finally combining the results. This segmentation avoids the mixing of universal and unique features that causes redundancy and damage in conventional weighted fusion methods.
Solution Approach 2:
The patent applies different fusion strategies to different feature types: universal features are fused with one approach while unique features are fused with another. This local quality approach ensures that each feature type is processed according to its specific characteristics, preventing feature damage while maintaining fusion effectiveness.
2Measurement precision
If professional physicians manually register and correct images to obtain effective connectivity, then the diagnosis accuracy is improved, but the time cost and labor cost increase significantly
Solution Approach 1:
The patent implements self-service through automated algorithms that perform image registration and connectivity analysis without requiring professional physician intervention. The system automatically processes medical images, extracts features, and generates diagnostic results, eliminating manual registration and correction steps while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of physician-based image registration and analysis with an automated computational system. Machine learning models and algorithms substitute for human expert operations, dramatically reducing time and labor costs while preserving diagnostic precision.
3Ease of manufacture
If conventional methods use software templates with specific parameters for connectivity calculation, then the process is standardized, but the output effect is greatly affected by parameter settings and personal experience
Solution Approach 1:
The patent dynamically adjusts parameters based on the specific characteristics of the input medical images and patient data, rather than using fixed software template parameters. The system learns optimal parameters through training on labeled data and adapts them during inference, eliminating the dependency on physician experience and template-specific settings while maintaining standardization.
Data Source
AI summary
The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.