AI Medical Compendium

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

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SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised imag...

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...

Improving Needle Tip Tracking and Detection in Ultrasound-Based Navigation System Using Deep Learning-Enabled Approach.

IEEE journal of biomedical and health informatics
Ultrasound-guided percutaneous interventions have numerous advantages over traditional techniques. Accurate needle placement in the target anatomy is crucial for successful intervention, and reliable visual information is essential to achieve this. H...

Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification.

IEEE journal of biomedical and health informatics
Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers...

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

IEEE journal of biomedical and health informatics
Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of ...

Compression and Encryption of Heterogeneous Signals for Internet of Medical Things.

IEEE journal of biomedical and health informatics
Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, i...

Auto Diagnosis of Parkinson's Disease Via a Deep Learning Model Based on Mixed Emotional Facial Expressions.

IEEE journal of biomedical and health informatics
Parkinson's disease (PD) is a common degenerative disease of the nervous system in the elderly. The early diagnosis of PD is very important for potential patients to receive prompt treatment and avoid the aggravation of the disease. Recent studies ha...

TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification.

IEEE journal of biomedical and health informatics
In clinical settings, the implementation of deep neural networks is impeded by the prevalent problems of label scarcity and class imbalance in medical images. To mitigate the need for labeled data, semi-supervised learning (SSL) has gained traction. ...

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

IEEE journal of biomedical and health informatics
EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. T...

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

IEEE journal of biomedical and health informatics
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary...