Radiology

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

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Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radio...

A deep convolutional neural network-based novel class balancing for imbalance data segmentation.

Retinal fundus images provide valuable insights into the human eye's interior structure and crucial ...

Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt d...

A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.

Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; how...

Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.

Brain tumor causes life-threatening consequences due to which its timely detection and accurate clas...

Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segment...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by inte...

Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis...

Knowledge mapping of ultrasound technology and triple-negative breast cancer: a visual and bibliometric analysis.

OBJECTIVE: This study aims to explore the application of ultrasound technology in triple-negative br...

Developments in MRI radiomics research for vascular cognitive impairment.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decli...

FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI.

Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabl...

Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (AL...

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.

Deep learning models require large amounts of annotated data, which are hard to obtain in the medica...

Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution.

Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues...

Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.

This study aimed to develop and validate convolutional neural network (CNN) models for distinguishin...

Magnetic resonance imaging of cruciate ligament disorders: current updates.

While conventional structural magnetic resonance imaging (MRI) can detect cruciate ligament anatomy ...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure...

F-FDG dose reduction using deep learning-based PET reconstruction.

BACKGROUND: A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistic...

Spondyloarthritis Research and Treatment Network (SPARTAN) Clinical and Imaging Year in Review 2024.

PURPOSE OF REVIEW: Diagnostic delay remains a critical challenge in axial spondyloarthritis (axSpA)....

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