Radiology

Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.

The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of datasets. However, due to the nature of the medical domain, there are limitations in the number of data available. This is because the cost of acquiring...

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often sm...

External validation and performance analysis of a deep learning-based model for the detection of intracranial hemorrhage.

PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH...

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.

Brain tumours (BT) affect human health owing to their location. Artificial intelligence (AI) is intended to assist in diagnosing and treating complex ...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In ...

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the chara...

Use of Artificial Intelligence in Imaging Dementia.

Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body ...

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning.

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neu...

Automatic 3-D Lamina Curve Extraction From Freehand 3-D Ultrasound Data Using Sequential Localization Recurrent Convolutional Networks.

Freehand 3-D ultrasound imaging is emerging as a promising modality for regular spine exams due to its noninvasiveness and affordability. The laminae ...

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing.

Autonomous ultrasound image quality assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the fut...

Automatic Segmentation of Abdominal Aortic Aneurysms From Time-Resolved 3-D Ultrasound Images Using Deep Learning.

Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored ...

Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography.

The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data...

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) i...

Active Inference and Deep Generative Modeling for Cognitive Ultrasound.

Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultraportable and cost-effectiv...

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classificati...

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.

In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and mic...

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in ...

Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI.

BACKGROUND: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformer...

Multi-scale region selection network in deep features for full-field mammogram classification.

Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early...