Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease
Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders, there often arises a problem of limited labeled da...
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MDPI AG
2025-02-01
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| author | Chang Hu Yihong Dong Shoubo Peng Yuehan Wu |
| author_facet | Chang Hu Yihong Dong Shoubo Peng Yuehan Wu |
| author_sort | Chang Hu |
| collection | DOAJ |
| description | Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders, there often arises a problem of limited labeled data due to factors such as large data volumes and cumbersome labeling processes, leading to the emergence of unlabeled data with new classes, which can result in misdiagnosis. In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. To improve open-world semi-supervised representation learning and classification in fMRI under low-label settings, we propose a novel open-world semi-supervised learning approach tailored for functional magnetic resonance imaging analysis, termed Open-World Semi-Supervised Learning for fMRI Analysis (OpenfMA). Specifically, we employ spectral augmentation self-supervised learning and dynamic concept contrastive learning to achieve open-world graph learning guided by pseudo-labels, and construct hard positive sample pairs to enhance the network’s focus on potential positive pairs. Experiments conducted on public datasets validate the superior performance of this method in the open-world psychiatric disease diagnosis domain. |
| format | Article |
| id | doaj-art-addf52fd05a24b639e85ebe1d8ef2c5b |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-addf52fd05a24b639e85ebe1d8ef2c5b2025-08-20T02:42:34ZengMDPI AGInformation2078-24892025-02-0116317110.3390/info16030171Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric DiseaseChang Hu0Yihong Dong1Shoubo Peng2Yuehan Wu3Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaDue to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders, there often arises a problem of limited labeled data due to factors such as large data volumes and cumbersome labeling processes, leading to the emergence of unlabeled data with new classes, which can result in misdiagnosis. In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. To improve open-world semi-supervised representation learning and classification in fMRI under low-label settings, we propose a novel open-world semi-supervised learning approach tailored for functional magnetic resonance imaging analysis, termed Open-World Semi-Supervised Learning for fMRI Analysis (OpenfMA). Specifically, we employ spectral augmentation self-supervised learning and dynamic concept contrastive learning to achieve open-world graph learning guided by pseudo-labels, and construct hard positive sample pairs to enhance the network’s focus on potential positive pairs. Experiments conducted on public datasets validate the superior performance of this method in the open-world psychiatric disease diagnosis domain.https://www.mdpi.com/2078-2489/16/3/171graph neural networksopen-world semi-supervised learningfMRI analysiscontrastive learning |
| spellingShingle | Chang Hu Yihong Dong Shoubo Peng Yuehan Wu Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease Information graph neural networks open-world semi-supervised learning fMRI analysis contrastive learning |
| title | Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease |
| title_full | Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease |
| title_fullStr | Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease |
| title_full_unstemmed | Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease |
| title_short | Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease |
| title_sort | open world semi supervised learning for fmri analysis to diagnose psychiatric disease |
| topic | graph neural networks open-world semi-supervised learning fMRI analysis contrastive learning |
| url | https://www.mdpi.com/2078-2489/16/3/171 |
| work_keys_str_mv | AT changhu openworldsemisupervisedlearningforfmrianalysistodiagnosepsychiatricdisease AT yihongdong openworldsemisupervisedlearningforfmrianalysistodiagnosepsychiatricdisease AT shoubopeng openworldsemisupervisedlearningforfmrianalysistodiagnosepsychiatricdisease AT yuehanwu openworldsemisupervisedlearningforfmrianalysistodiagnosepsychiatricdisease |