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Computer Methods and Programs in Biomedicine|January 15, 2025
Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic modelLi Chen, Zhong Yin, Xuelin Gu, et al.
Computer Methods and Programs in Biomedicine|January 8, 2025
An emotionally intelligent haptic system - An efficient solution for anxiety detection and mitigationSwapneel Mishra, Saumya Seth, Shrishti Jain, et al.
Computer Methods and Programs in Biomedicine|January 9, 2025
Leveraging Transformers-based models and linked data for deep phenotyping in radiologyLluís-F Hurtado, Luis Marco-Ruiz, Encarna Segarra, et al.
Computer Methods and Programs in Biomedicine|January 1, 2025
Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generationYintong Liu, U Rajendra Acharya, Jen Hong Tan
Computer Methods and Programs in Biomedicine|March 26, 2025
Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamicsGhada Alhussein, Mohanad Alkhodari, Ioannis Ziogas, et al.
Computer Methods and Programs in Biomedicine|March 12, 2025
A comprehensive exploration of motion sickness process analysis from EEG signal and virtual realityNaishi Feng, Bin Zhou, Qianqian Zhang, et al.
Computer Methods and Programs in Biomedicine|March 13, 2025
Natural Language Processing and soft data for motor skill assessment: A case study in surgical training simulationsArash Iranfar, Mohammad Soleymannejad, Behzad Moshiri, et al.
Computer Methods and Programs in Biomedicine|March 7, 2025
Preoperative ECG-assisted feature engineering enhances prediction of new-onset atrial fibrillation after cardiac surgeryBiqi Tang, Kang He, Sen Liu, et al.
Computer Methods and Programs in Biomedicine|April 4, 2025
Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense predictionXiang Liu, James Liang, Jianwei Zhang, et al.
Computer Methods and Programs in Biomedicine|April 5, 2025
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection modelsDaria Zotova, Nicolas Pinon, Robin Trombetta, et al.
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