Radiology

Toward Integrating Federated Learning With Split Learning via Spatio-Temporal Graph Framework for Brain Disease Prediction.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the oth...

Deep Learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope.

Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Mor...

Masked Deformation Modeling for Volumetric Brain MRI Self-Supervised Pre-Training.

Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated data and to improve downstream tasks' performance...

Automated chick gender determination using optical coherence tomography and deep learning.

Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by...

Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous inten...

A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair.

OBJECTIVE: This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to pre...

Optimized attention-enhanced U-Net for autism detection and region localization in MRI.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after sympto...

Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.

Segmentation of the breast region in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for the automatic measurement of brea...

AI-ready rectal cancer MR imaging: a workflow for tumor detection and segmentation.

BACKGROUND: Magnetic Resonance (MR) imaging is the preferred modality for staging in rectal cancer; however, despite its exceptional soft tissue contr...

Deep learning for the prediction of acute kidney injury after coronary angiography and intervention in patients with chronic kidney disease: a model development and validation study.

BACKGROUND: Patients with chronic kidney disease (CKD) are considered the primary population at risk for post-contrast acute kidney injury (PC-AKI), y...

DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer.

OBJECTIVE: To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict K...

Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted percep...

Deep learning in nuclear medicine: from imaging to therapy.

BACKGROUND: Deep learning, a leading technology in artificial intelligence (AI), has shown remarkable potential in revolutionizing nuclear medicine.

Deep learning-based automated segmentation of cardiac real-time MRI in non-human primates.

Advanced imaging techniques, like magnetic resonance imaging (MRI), have revolutionised cardiovascular disease diagnosis and monitoring in humans and ...

ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variabilit...

Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.

The segmentation of liver margins in computed tomography (CT) images presents significant challenges due to the complex anatomical variability of the ...

Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.

Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks ha...

Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.

BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective tre...

A brief history and clinical use of robotic procedures in the cardiovascular system.

Robotic-assisted percutaneous coronary intervention (r-PCI) exemplifies the advancement of interventional cardiology by integrating robotics to improv...

Association between muscle mass assessed by an artificial intelligence-based ultrasound imaging system and quality of life in patients with cancer-related malnutrition.

INTRODUCTION: Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in indiv...