AIMC Topic: Deep Learning

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Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

IEEE journal of biomedical and health informatics
Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable pe...

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

IEEE journal of biomedical and health informatics
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the e...

Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images.

IEEE journal of biomedical and health informatics
Kneeosteoarthritis (KOA), as a leading joint disease, can be decided by examining the shapes of patella to spot potential abnormal variations. To assist doctors in the diagnosis of KOA, a robust automatic patella segmentation method is highly demande...

Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.

IEEE journal of biomedical and health informatics
Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely ...

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

IEEE journal of biomedical and health informatics
Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively...

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

IEEE journal of biomedical and health informatics
CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disea...

Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement.

IEEE journal of biomedical and health informatics
Deep learning-based methods have been widely used in medical image segmentation recently. However, existing works are usually difficult to simultaneously capture global long-range information from images and topological correlations among feature map...

Magnetoencephalography Decoding Transfer Approach: From Deep Learning Models to Intrinsically Interpretable Models.

IEEE journal of biomedical and health informatics
When decoding neuroelectrophysiological signals represented by Magnetoencephalography (MEG), deep learning models generally achieve high predictive performance but lack the ability to interpret their predicted results. This limitation prevents them f...

Deep Learning-Based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli.

IEEE journal of biomedical and health informatics
Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional dia...

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

IEEE journal of biomedical and health informatics
Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in sur...