A Semi-supervised Classification RBM with an Improved fMRI Representation Algorithm

• Published in: Computer methods and programs in biomedicine 2022-07, Vol.222, p.106960-106960, Article 106960
• Main Authors: Can, Chang, Ning, Liu, Li, Yao, Xiaojie, Zhao
• Format: Article
• Language: English
• Subjects: Deep learning; Feature representation; functional magnetic resonance imaging (fMRI); restricted Boltzmann machine (RBM); semi-supervised classification
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106960

•An unsupervised feature learning algorithm named HRBM is used for RBM to make the fMRI feature representation learned sparse.•A semi-supervised classification RBM for fMRI with a joint tuning algorithm based on the improved HRBM, namely semi-HRBM is proposed.•Compared with the supervised models, the performance of Semi-HRBM was significantly improved.•Our HRBM has satisfactory feature representation capabilities and better performance for multiple classification tasks.•Our Semi-HRBM classification model improves the average accuracy of the four-classification task by 7.68%, and improves the average F1 score of each visual stimulus task by 8.90%. Training an effective and robust supervised learning classifier is not easy due to the limitations of acquiring and labeling considerable human functional magnetic resonance imaging (fMRI) data. Semi-supervised learning uses unlabeled data for feature learning and combines them into labeled data to build better classification models. : Since no premises or assumptions are required, a restricted Boltzmann machine (RBM) is suitable for learning data representation of neuroimages. In our study, an improved fMRI representation algorithm with a hybrid L1/L2 regularization method (HRBM) was proposed to optimize the original model for sparsity. Different from common semi-supervised classification models that treat feature learning and classification as two separate training steps, we then constructed a new semi-supervised classification RBM based on a joint training algorithm with HRBM, named Semi-HRBM. This joint training algorithm jointly trains the objective function of feature learning and classification process, so that the learned features can effectively represent the original fMRI data and adapt to the classification tasks. This study uses fMRI data to identify categories of visual stimuli. In the fMRI data classification task under four visual stimuli (house, face, car, and cat), our HRBM has satisfactory feature representation capabilities and better performance for multiple classification tasks. Taking the supervised RBM (sup-RBM) as an example, our Semi-HRBM classification model improves the average accuracy of the four-classification task by 7.68%, and improves the average F1 score of each visual stimulus task by 8.90%. In addition, the generalization ability of the model was also improved. This research might contribute to enrich solutions for insufficiently labeled neuroimaging samples, which could help to