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Found 4 papers, 2 papers with code
Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases Detection
It enables a robust adaptation of pre-trained models on downstream datasets with limited supervision and high computational efficiency.
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition
The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6. 89% improvement on SEED and 1. 44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals.
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition
The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases.
