Abstract: We present a novel deep neural architecture for learning electroencephalogram (EEG). To learn the spatial information, our model first obtains the Riemannian mean and distance from spatial ...
Abstract: The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented ...
Hai L. Vu is a Professor and the recipient of the Australian Research Council (ARC) Future Fellow Award (2012-2016) in the area of Intelligent Transport Systems (ITS). He joined Monash University in ...
This article discuss if the US government will regulated the most advanced AI models. It argues that we have hit a cap on the ...
Diabetic Retinopathy 224x224 2019 dataset (fundus images). The dataset is imbalanced: “Moderate” DR has the most samples, while “Mild” and “Proliferative DR” are underrepresented. We use class weights ...
Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...
Domain-aware domain–class adaptation network for motor execution to motor imagery EEG classification
Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Joint first authors with equal contribution.
Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems - Volume III ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results