Progress in Deep Learning for Medical Image and EEG Classification

In recent years, deep learning has been enjoying many successful applications in the entire spectrum of technology. This talk highlights some of our recent research results in deep learning for medical image and EEG classification. Our algorithms include 3D convolutional neural networks (CNNs) with thresholding and attention, a transformer-based multilevel filtering framework, batch normalization with domain-matching, and a time-frequency transformer (TFormer). We demonstrate our algorithms in various challenging problems, such as brain trauma diagnosis, glaucoma diagnosis, emotion and fatigue recognition based on multi-subject EEG signal classification.