Disentanglement and codebook learning-induced feature match network to diagnose neurodegenerative diseases on incomplete multimodal data

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wei Xiong , Tao Wang , Xiumei Chen , Yue Zhang , Wencong Zhang , Qianjin Feng , Meiyan Huang , Alzheimer’s Disease Neuroimaging Initiative
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引用次数: 0

Abstract

Multimodal data can provide complementary information to diagnose neurodegenerative diseases (NDs). However, image quality variations and high costs can result in the missing data problem. Although incomplete multimodal data can be projected onto a common space, the traditional projection process may increase alignment errors and lose some modality-specific information. A disentanglement and codebook learning-induced feature match network (DCFMnet) is proposed in this study to solve the aforementioned issues. First, multimodal data are disentangled into latent modality-common and -specific features to help preserve modality-specific information in the subsequent alignment of multimodal data. Second, the latent modal features of all available data are aligned into a common space to reduce alignment errors and fused to achieve ND diagnosis. Moreover, the latent modal features of the modality with missing data are explored in online updated feature codebooks. Last, DCFMnet is tested on two publicly available datasets to illustrate its excellent performance in ND diagnosis.
解缠和码本学习诱导特征匹配网络在不完全多模态数据上诊断神经退行性疾病
多模态数据可为神经退行性疾病的诊断提供补充信息。然而,图像质量的变化和高成本会导致数据丢失问题。虽然不完整的多模态数据可以投影到公共空间,但传统的投影过程可能会增加对齐误差并丢失一些特定于模态的信息。为了解决上述问题,本文提出了一种解纠缠和码本学习诱导的特征匹配网络(DCFMnet)。首先,将多模态数据分解为潜在的模态共同特征和特定特征,以帮助在随后的多模态数据对齐中保留特定于模态的信息。其次,将所有可用数据的潜在模态特征对齐到一个公共空间,以减少对齐误差并进行融合以实现ND诊断;此外,在在线更新的特征码本中,研究了缺失数据模态的潜在模态特征。最后,在两个公开可用的数据集上对DCFMnet进行了测试,以说明其在ND诊断中的优异性能。
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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