EPDTNet + -EM: Advanced Transfer Learning and Subnet Architecture for Medical Image Diagnosis

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
medical image classification   enhanced exponential transfer learning   parallel subnet (PSNet +)   explainable AI (XAI)   feature extraction   abnormality detection   computational efficiency  

Academic Background In today’s healthcare environment, medical imaging plays a crucial role in disease diagnosis, treatment planning, and health management. However, traditional medical image analysis methods face numerous challenges, such as overfitting, high computational costs, limited generalization capabilities, and issues related to noise, si...

Continual Learning of Conjugated Visual Representations through Higher-Order Motion Flows

Information Science, Computer Science, Artificial Intelligence,
continual learning   motion flows   self-supervised learning   feature extraction   higher-order motion   visual representations   online learning  

Continual Learning of Conjugated Visual Representations through Higher-Order Motion Flows: A Study on the CMOSFET Model Academic Background In the fields of artificial intelligence and computer vision, continual learning from continuous visual data streams has long been a challenge. Traditional machine learning methods typically rely on the assumpt...

Enhancing Aerial Object Detection with Selective Frequency Interaction Network

Engineering, Aerospace Engineering, Information Science, Computer Science, Artificial Intelligence,
aerial object detection   frequency-domain features   selective frequency interaction   convolutional neural networks   feature extraction   deep learning   computer vision  

Selective Frequency Interaction Network for Improved Aerial Object Detection Background and Problem Statement With the advancements in computer vision, aerial object detection has become a critical research focus in remote sensing. This task aims to identify targets such as vehicles or buildings from aerial images captured at varying angles and alt...

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology,
sleep stage scoring   deep learning   single-channel EEG   convolutional neural networks   bidirectional LSTMs   feature extraction   automation  

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...