Missing multi-modal representation learning algorithm for Alzheimer's disease diagnosis

Abstract

The invention provides a missing multi-modal representation learning algorithm for Alzheimer's disease diagnosis. The invention provides a brand-new missing multi-modal data completion model. The missing multi-modal representation learning algorithm for Alzheimer's disease diagnosis comprises the following steps: firstly, mapping original complete modal data to a hidden space by using an automaticencoder network; then supplementing a kernel matrix of an incomplete view in the kernel space by utilizing potential representation learned from the complete mode, meanwhile, constraining the correlation between samples and between modes through graph regularization and an HSIC algorithm, and finally mapping the kernel matrix into a feature matrix through correlation analysis of a kernel method to complete a subsequent diagnosis task. The Alzheimer's disease can be rapidly and accurately diagnosed under the condition that certain modal data are lost, and the algorithm has good performance andobvious advantages, can be applied to scenes such as assisting in diagnosis of doctors and community screening, and has a good development prospect along with development of a population aging process.

Description

technical field [0001] The invention relates to a missing multimodal representation learning algorithm for the diagnosis of Alzheimer's disease, and belongs to the fields of machine learning and medical image analysis. Background technique [0002] Alzheimer's disease (AD) and its early stages are an irreversible neurodegenerative disease that has a serious impact on people's health worldwide. Alzheimer's disease is usually difficult to cure, However, early comprehensive treatment can alleviate the condition and delay the development, so early diagnosis and treatment are of great significance to improve the quality of life of patients. [0003] Computer-aided diagnosis of Alzheimer's disease through machine learning can improve diagnostic efficiency, and a large number of studies have shown that computer-aided diagnosis methods combined with multimodal data can further improve diagnostic accuracy. However, in practical applications, we often face the situation of missing mu...

AI Technical Summary

Problems solved by technology

However, in practical applications, we often face the situation of missing multimodal data (that is, not all modal data are complete). Most of the multimodal learning algorithms are not able to effectively deal with missing multimodal data

Images