Method for establishing brain glioma grading hybrid network based on multi-view feature fusion

By constructing a hybrid network for glioma grading that integrates multi-view features, and utilizing multi-view MRI data and an innovative feature extraction module, the problems of time-consuming, labor-intensive, and information-loss-prone traditional imaging diagnostic methods are solved, enabling comprehensive understanding and efficient classification of gliomas.

CN119693698BActive Publication Date: 2026-04-10ZHENGZHOU UNIV
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Patent Information

Application Number
CN202411758380.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2026-04-10
Estimated Expiration
2044-12-02

AI Technical Summary

Technical Problem

Traditional imaging diagnostic methods rely on physician experience, are time-consuming and labor-intensive, and are highly subjective. Single-modal MRI analysis cannot capture the heterogeneity and complexity within tumors, while multimodal MRI fusion methods ignore scanning planes, leading to information loss. Attention mechanisms are not good at extracting local features, affecting the accuracy and reliability of classification results.

Method used

A hybrid network for glioma grading based on multi-view feature fusion is constructed. Through data preprocessing, feature extraction and multi-view feature fusion, MRI data from inter-slice, spatial and modal perspectives are used. Global, local and multi-scale feature extraction is performed by combining focused overlapping spatial reduction attention and adaptive dilated convolution. Feature expression is enhanced by a multi-view feature fusion module.

Benefits of technology

This approach enables a comprehensive understanding of gliomas, balances the global receptive field and inductive bias, enhances feature extraction capabilities, and improves the accuracy and reliability of classification results.

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Abstract

The application discloses a method for establishing a brain glioma grading hybrid network based on multi-view feature fusion, which comprises four steps of data preprocessing, feature extraction, multi-view feature fusion and grading processing of the obtained features through a classifier; the method can balance global receptive field and induction bias, combines global and local multi-scale and multi-granularity features, balances global receptive field and induction bias at the same time, and enhances feature extraction capability; by constructing different view data sources (interlayer view, scanning plane view and modality view) and designing a novel feature fusion module, the network can fully and effectively utilize key information provided by different scanning planes and different modalities of MRI, including interlayer, spatial and multi-modality information, and realizes comprehensive cognition of brain glioma.
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