Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.

The application of machine learning to tasks involving volumetric biomedical imaging is constrained ...

Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI.

BACKGROUND: Automated approaches may allow for fast, reproducible clinical assessment of cardiovascu...

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.

PURPOSE: Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra...

Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.

PURPOSE: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer...

Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.

Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to s...

Deep learning model for automated diagnosis of degenerative cervical spondylosis and altered spinal cord signal on MRI.

BACKGROUND CONTEXT: A deep learning (DL) model for degenerative cervical spondylosis on MRI could en...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functiona...

Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a val...

Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment.

BACKGROUND: The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) req...

Machine Learning-Empowered Real-Time Acoustic Trapping: An Enabling Technique for Increasing MRI-Guided Microbubble Accumulation.

Acoustic trap, using ultrasound interference to ensnare bioparticles, has emerged as a versatile too...

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.

Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechani...

Artificial intelligence in the radiological diagnosis of cancer.

Artificial intelligence (AI) is being used to diagnose deadly diseases such as cancer. The possible ...

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging.

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the...

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D...

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.

Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase th...

Label refinement network from synthetic error augmentation for medical image segmentation.

Deep convolutional neural networks for image segmentation do not learn the label structure explicitl...

A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying met...

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