Graph convolutional network with sample and feature weights for Alzheimer's disease diagnosis. Issue 4 (July 2022)

• Record Type: Journal Article
• Authors: Zeng, Lu; Li, Hengxin; Xiao, Tingsong; Shen, Fumin; Zhong, Zhi
• Is Part Of: Information processing & management. Volume 59:Issue 4(2022)
• Publication Date: 2022-07
• Subjects: Alzheimer's disease diagnosis -- GCN -- Training set bias -- Interpretability; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102952
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Either traditional learning methods or deep learning methods have been widely applied for the early Alzheimer's disease (AD) diagnosis, but these methods often suffer from the issue of training set bias and have no interpretability. To address these issues, this paper proposes a two-phase framework to iteratively assign weights to samples and features. Specifically, the first phase automatically distinguishes clean samples from training samples. Training samples are regarded as noisy data and thus should be assigned different weights for penalty, while clean samples are of high quality and thus are used to learn the feature weights. In the second phase, our method iteratively assigns sample weights to the training samples and feature weights to the clean samples. Moreover, their updates are iterative so that the proposed framework deals with the training set bias issue as well as contains interpretability on both samples and features. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that our method achieves the best classification performance in terms of binary classification tasks and has better interpretability, compared to the state-of-the-art methods. Highlights: This paper considers the feature importance to solve the training set bias issue. This paper considers real-time interpretability on both samples and features. The data pre-processing method automatically selects clean samples in noisy dataset. Extensive experiments verify the effectiveness of the proposed method on AD datasets.