Radiology

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for preventing progression and formulating effective treatment plans. This study aims to develop a novel deep learning (DL) model, Hybrid-RViT, to enhance the detection of AD. The proposed Hybrid-RViT model integrates the pre-trained convolutional neural network (ResNet-50) with the Vision Transformer ...

Deep transfer learning based hierarchical CAD system designs for SFM images.

Present work involves rigorous experimentation for classification of mammographic masses by employing four deep transfer learning models using hierarchical framework. Experimental work is carried on 518 SFM images of DDSM dataset with 208, 150 and 160 images of probably benign, suspicious- malignant and highly malignant classes, respectively. ResNet50 model is used for generating segmented mass im...

Improved segmentation of hepatic vascular networks in ultrasound volumes using 3D U-Net with intensity transformation-based data augmentation.

Accurate three-dimensional (3D) segmentation of hepatic vascular networks is crucial for supporting ultrasound-mediated theranostics for liver disease...

Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning.

OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging ...

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.

PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver...

Diagnosis and treatment of a rare bilateral primary thyroid cancer: a case report.

Preoperative ultrasound examination of thyroid nodules is the most economical and effective screening method for diagnosing thyroid nodules. Fine-need...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images....

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when inter...

Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.

PURPOSE: This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (D...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemo...

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.

One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The curr...

Diffusion-driven multi-modality medical image fusion.

Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic in...

Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT i...

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.

Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential pa...

New Perspectives and State of the Art in Interventional Radiotherapy (Brachytherapy) for Head and Neck Tumors.

AIM: This review aims to evaluate the role of interventional radiotherapy (IRT - brachytherapy) in the clinical management of head and neck (H&N) canc...

The Evolution of Artificial Intelligence in Nuclear Medicine.

Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration ...

Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.

RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develo...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. ...

Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.

Peripheral artery disease (PAD) remains underdiagnosed and undertreated and is associated with an increased risk for adverse cardiovascular outcomes. ...

Attention-Based Q-Space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusi...