Topology-aware deep learning model enhances EEG-based motor imagery decoding

Electroencephalography (EEG) is a fascinating noninvasive technique that measures and records the brain's electrical activity ...
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
IEEE

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification

Abstract: This study presents a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing the impact of complementary feature domains on model ...
Frontiers

A novel MRI-based deep learning–radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection

Introduction: Accurate and timely diagnosis of central nervous system infections (CNSIs) is critical, yet current gold-standard techniques like lumbar puncture (LP) remain invasive and prone to delay.
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DenseNet Architecture Explained | Deep Learning Tutorial for Beginners

Learn how DenseNet works and why it’s a powerful architecture in deep learning. This tutorial breaks down DenseNet’s key concepts, including dense connections, feature reuse, and parameter efficiency ...
Scientific Research Publishing

Martinez, C., Nguyen, L. and Patel, A. (2023) RF-Vit-Gan: Unsupervised RF Signal Synthesis Using Vision Transformers. NeurIPS Workshop on Deep Learning for Physical Sciences ...

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...
IEEE

ECG_DEEPNet: A Novel Approach for Delineation and Classification of Electrocardiogram Signal Based on Ensemble Deep-Learning

Abstract: The advancements in telehealth monitoring technology have enabled the collection of vast quantities of electrophysiological signals, including the electrocardiogram (ECG) which contains ...