AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Privacy-Preserving Federated Learning With Domain Adaptation for Multi-Disease Ocular Disease Recognition.

IEEE journal of biomedical and health informatics
As one of the effective ways of ocular disease recognition, early fundus screening can help patients avoid unrecoverable blindness. Although deep learning is powerful for image-based ocular disease recognition, the performance mainly benefits from a ...

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

IEEE journal of biomedical and health informatics
Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficu...

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

IEEE journal of biomedical and health informatics
Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the ski...

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

IEEE journal of biomedical and health informatics
The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And ...

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...

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

IEEE journal of biomedical and health informatics
Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly...

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...

An Efficient and Rapid Medical Image Segmentation Network.

IEEE journal of biomedical and health informatics
Accurate medical image segmentation is an essential part of the medical image analysis process that provides detailed quantitative metrics. In recent years, extensions of classical networks such as UNet have achieved state-of-the-art performance on m...

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 ...