AI Medical Compendium Journal:
IEEE journal of biomedical and health informatics

Showing 21 to 30 of 1081 articles

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

IEEE journal of biomedical and health informatics
Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly re...

A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning.

IEEE journal of biomedical and health informatics
This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their application in cardiac health monitoring. This study focuses on characteristics crucial to these dev...

Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.

IEEE journal of biomedical and health informatics
Breast cancer is a pervasive global health concern among women. Leveraging multimodal data from enterprise patient databases-including Picture Archiving and Communication Systems (PACS) and Electronic Health Records (EHRs)-holds promise for improving...

Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-Modal Brain Networks.

IEEE journal of biomedical and health informatics
The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the int...

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning.

IEEE journal of biomedical and health informatics
Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preopera...

Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension.

IEEE journal of biomedical and health informatics
Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a promising way to improve the clinical diagnostic experience by significantly reducing scan times. While existing studies have used Generative Adversarial Networks to...

MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.

IEEE journal of biomedical and health informatics
Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there ...

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with...

PMMNet: A Dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation.

IEEE journal of biomedical and health informatics
Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are esse...

Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics
In the context of contemporary artificial intelligence, increasing deep learning (DL) based segmentation methods have been recently proposed for brain tumor segmentation (BraTS) via analysis of multi-modal MRI. However, known DL-based works usually d...