Learning on sample-efficient and label-efficient multi-view cardiac data with graph transformer

• Published in: Pattern recognition letters Vol. 180; pp. 127 - 133
• Main Authors: Wang, Lujing, Ma, Yunting, Zhang, Wanqiu, Zhao, Xiaoying, Zhao, Xinxiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: Cardiovascular disease; Graph transformer; Label-efficient; Multi-view learning; Sample-efficient; Cardiovascular disease; Graph transformer; Label-efficient; Multi-view learning; Sample-efficient
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2024.03.001

Predicting cardiovascular disease has been a challenging task, as assessing samples based on a single view of information may be insufficient. Therefore, in this paper, we focus on the challenge of predicting cardiovascular disease using multi-view cardiac data. However, multi-view cardiac data is usually difficult to collect and label. Based on this motivation, learning an effective predictive model on sample-efficient and label-efficient multi-view cardiac data is urgently needed. To address the aforementioned issues, we propose a multi-view learning method: (i) our method utilizes graph learning to establish and extract relationships between data, enabling learning from a small number of labeled data and a small number of samples; (ii) our method integrates features from multiple views to utilize complementary information in the data; (iii) for data without a provided graph of relationships between samples, we utilize the mechanism of transformers to learn the relationships between samples in a data-driven manner. We validate the effectiveness of our method on real heart disease datasets. •Our method considers multi-view cardiac data to provide comprehensive and accurate information for diagnosis.•Our method overcomes the limitations of sample-efficient and label-efficient data.•Our method captures global relationships between subjects and achieves high diagnostic accuracy.