基于多模态模糊特征融合的脑龄协同预测算法

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中文摘要：深度神经网络可通过训练从大脑图像中预测年龄,作为识别衰老相关疾病的生物标志物.传统的脑龄预测方法往往依赖于单一模态的图像数据,而多模态数据可提供更全面的信息,提高预测精度.然而,现有的多模态融合方法往往不能充分利用不同模态之间的相关性和互补性.为了克服上述问题,文中提出基于多模态模糊特征融合的脑龄协同预测算法(CMFF),设计模糊融合模块和多模态协同卷积模块,可有效利用多模态信息之间的相关信息和互补信息.首先,利用卷积神经网络从多模态脑图中提取特征张量,径向拼接后整合到一个全局特征张量中.然后,利用模糊融合模块学习被模糊化的特征,再将特征应用到多模态协同卷积模块,通过特定的卷积层增强模态间的互补信息.最后,基于性别信息和经过模糊协同处理的特征执行年龄预测回归任务,得到准确的预测年龄.在SRPBS多重障碍MRI数据集上的实验表明,CMFF性能较优.

外文标题：Collaborative Brain Age Prediction Algorithm Based on Multimodal Fuzzy Feature Fusion

外文摘要：Deep neural networks can be trained to predict age from brain image and the predicted brain age serves as a biomarker for identifying diseases associated with aging.Traditional brain age prediction methods tend to rely on unimodal image data,whereas multimodal data can provide more comprehensive information and improve prediction accuracy.However,existing multimodal fusion methods often fail to fully exploit the correlations and complementarities between different modalities.To overcome these challenges,a collaborative brain age prediction algorithm based on multimodal fuzzy feature fusion(CMFF)is proposed.Fuzzy fusion module and multimodal collaborative convolution module are designed to effectively utilize the correlation and complementary information between the multimodal information.Firstly,feature tensors are extracted from multimodal brain images by a convolutional neural network,and are integrated into a global feature tensor via radial joins.Then,the fuzzy fusion module is employed to learn the fuzzified features,and these features are applied to the multimodal collaborative convolutional module to enhance the complementary information of these features through modality-specific convolutional layers.Finally,the age prediction regression task is performed based on the gender information and the fuzzy collaborative processed features to obtain an accurate predicted age.Experimental results on SRPBS multi-disorder MRI dataset demonstrate the superior performance of CMFF.

外文关键词：

Fuzzy FusionCollaborative ConvolutionBrain Age PredictionMultimodal Medical ImageDeep Learning

作者：

王静、丁卫平、尹涛、鞠恒荣、黄嘉爽

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作者单位：

南通大学人工智能与计算机学院南通 226019

关键词：

模糊融合协同卷积脑龄预测多模态医学影像深度学习

基金：

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目江苏省自然科学基金项目江苏省双创博士计划项目江苏省高等学校自然科学研究重大项目江苏省高等学校自然科学研究面上项目南通市科技局基础科学研究项目中国博士后科学基金项目

项目编号：

619761206200612862102199BK20231337JSSCBS2023034821KJA51000423KJB520031JC20211222022M711716

出版年：

2024

DOI：

10.16451/j.cnki.issn1003-6059.202407004

模式识别与人工智能

中国自动化学会,国家智能计算机研究开发中心,中国科学院合肥智能机械研究所

模式识别与人工智能

CSTPCD北大核心

影响因子：0.954

ISSN：1003-6059

年,卷(期)：2024.37(7)

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