Brain glioma grading method based on multi-instance causal characterization learning

By employing a multi-instance causal representation learning method, combined with causal perception attention and dynamic aggregation mechanisms, the heterogeneity and complexity of MRI images of gliomas are addressed, improving the stability and clinical applicability of the grading model and providing a reliable auxiliary diagnostic tool.

CN120931573APending Publication Date: 2025-11-11THE FIRST AFFILIATED HOSPITAL OF ZHENGZHOU UNIV
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Patent Information

Application Number
CN202511023526.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-24
Publication Date
2025-11-11

AI Technical Summary

Technical Problem

Existing automated grading methods for gliomas are not robust enough to handle the high heterogeneity and complexity of MRI images, lack generalization ability, and are not interpretable enough to meet clinical needs.

Method used

A multi-instance causal representation learning method is adopted. By treating glioma MRI slices as multiple independent instances, the method combines causal reasoning with graph neural networks, and introduces causal perception attention mechanism and causal perception dynamic aggregation mechanism to achieve dynamic weight allocation and deep structural modeling of multi-instance features.

Benefits of technology

It significantly improves the robustness and generalization ability of the classification, enhances the interpretability of the model, provides a more reliable auxiliary diagnostic tool, and solves the problems of model instability and interpretability in existing technologies.

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Abstract

The invention relates to a brain glioma grading method based on multi-instance causal characterization learning, and the method comprises the following steps: S1, data preprocessing, S2, feature extraction through ResNet-50, S3, feature coding of high-dimensional feature representations of instances, S4, decoupling of low-dimensional feature representations of each instance, obtaining causal features and non-causal features, and carrying out classification on the causal features and the non-causal features. S5, constructing an adaptive adjacency matrix A, constructing a sparse adjacency matrix A (k) by adopting a Topk neighbor selection strategy, and carrying out information aggregation through a graph neural network to obtain aggregation features; S6, carrying out local feature aggregation on each scanning plane p, calculating a weight weighted p of each scanning plane by utilizing a full-connection network, and carrying out weighted fusion to obtain an aggregation feature; s7, fusing and grading the aggregated features and the features; the method has the advantage of improving the stability and clinical applicability of the grading model.
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