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Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images
Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images
Hydatid cysts, caused by Echinococcus granulosus, form progressively enlarging fluid-filled cysts in organs like the liver...
Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study
Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study
Education for medical imaging technologists or radiographers in regional and rural areas often faces significant challenge...
Deep Learning Approach for Biomedical Image Classification
Deep Learning Approach for Biomedical Image Classification
Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcome...
Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis
Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis
Dysphagia manifests a wide spectrum of effects on human health, ranging from mild symptoms like coughing to more severe is...
Inter-AI Agreement in Measuring Cine MRI-Derived Cardiac Function and Motion Patterns: A Pilot Study
Inter-AI Agreement in Measuring Cine MRI-Derived Cardiac Function and Motion Patterns: A Pilot Study
Manually analyzing a series of MRI images to obtain information about the heart’s motion is a time-consuming and lab...
Interpretable MRI Subregional Radiomics-Deep Learning Model for Preoperative Lymphovascular Invasion Prediction in Rectal Cancer: A Dual-Center Study
Interpretable MRI Subregional Radiomics-Deep Learning Model for Preoperative Lymphovascular Invasion Prediction in Rectal Cancer: A Dual-Center Study
Develop a fusion model based on explainable machine learning, combining multiparametric MRI subregional radiomics and deep...
ESE and Transfer Learning for Breast Tumor Classification
ESE and Transfer Learning for Breast Tumor Classification
In this study, we proposed a lightweight neural network architecture based on inverted residual network, efficient squeeze...
Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques
Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques
Renal cancer is amid the several reasons of increasing mortality rates globally, which can be reduced by early detection a...
An Adaptive Generative 3D VNet Model for Enhanced Monkeypox Lesion Classification Using Deep Learning and Augmented Image Fusion
An Adaptive Generative 3D VNet Model for Enhanced Monkeypox Lesion Classification Using Deep Learning and Augmented Image Fusion
As monkeypox is spreading rapidly, the incidence of monkeypox has been increasing in recent times. Therefore, it is very i...
Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae
Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae
This work uses T1, STIR, and T2 MRI sequences of the lumbar vertebrae and BMD measurements to identify osteoporosis using ...
Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis
Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis
We conducted a systematic review and meta-analysis in diagnostic performance of studies that tried to use artificial intel...
AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations
AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations
Artificial intelligence (AI)-driven autonomous agents are transforming multiple domains by integrating reasoning, decision...
A Two-Phase Deep Learning Approach for Architectural Distortion Detection in Mammograms
A Two-Phase Deep Learning Approach for Architectural Distortion Detection in Mammograms
Breast cancer remains a global health challenge, ranking as a leading cause of mortality among women worldwide, emphasizin...
Automatic Prediction of TMJ Disc Displacement in CBCT Images Using Machine Learning
Automatic Prediction of TMJ Disc Displacement in CBCT Images Using Machine Learning
Magnetic resonance imaging (MRI) is the gold standard for diagnosing disc displacement in temporomandibular joint (TMJ) di...
Could a New Method of Acromiohumeral Distance Measurement Emerge? Artificial Intelligence vs. Physician
Could a New Method of Acromiohumeral Distance Measurement Emerge? Artificial Intelligence vs. Physician
The aim of this study was to evaluate the reliability of ChatGPT-4 measurement of acromiohumeral distance (AHD), a popular...
Privacy-Preserving Generation of Structured Lymphoma Progression Reports from Cross-sectional Imaging: A Comparative Analysis of Llama 3.3 and Llama 4
Privacy-Preserving Generation of Structured Lymphoma Progression Reports from Cross-sectional Imaging: A Comparative Analysis of Llama 3.3 and Llama 4
Efficient processing of radiology reports for monitoring disease progression is crucial in oncology. Although large langua...
3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention
3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention
Multimodal image registration is crucial in medical imaging, particularly for aligning Magnetic Resonance Imaging (MRI) an...
XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis
XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis
Chest X-ray is one of the main diagnostic tools in emergency medicine, yet its limited ability to capture fine anatomical ...
Exploring AI-Based System Design for Pixel-Level Protected Health Information Detection in Medical Images
Exploring AI-Based System Design for Pixel-Level Protected Health Information Detection in Medical Images
De-identification of medical images is a critical step to ensure privacy during data sharing in research and clinical sett...
Development and Evaluation of an Automated Histomorphometric Analysis Method for the Assessment of Implant Osseointegration
Development and Evaluation of an Automated Histomorphometric Analysis Method for the Assessment of Implant Osseointegration
This study evaluated an automated histomorphometric analysis method for assessing implant osseointegration. Sixty-eight hi...
A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation
A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation
The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient’...
Integrating Global Health Initiatives into Routine Radiology Workflow in the USA
Integrating Global Health Initiatives into Routine Radiology Workflow in the USA
Radiologist shortages and lack of access to radiology services are common issues in low- and middle-income countries aroun...
Diagnosis of Acute Versus Chronic Thoracolumbar Vertebral Compression Fractures Using CT Radiomics Based on Machine Learning: a Preliminary Study
Diagnosis of Acute Versus Chronic Thoracolumbar Vertebral Compression Fractures Using CT Radiomics Based on Machine Learning: a Preliminary Study
The purpose of this study is to evaluate the performance of radiomic models in acute thoracolumbar vertebral compression f...
Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients
Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients
Hemorrhagic transformation (HT) is a potentially catastrophic complication after acute ischemic stroke. Prevention of HT r...
Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis
Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis
Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of he...
ColonNeXt: Fully Convolutional Attention for Polyp Segmentation
ColonNeXt: Fully Convolutional Attention for Polyp Segmentation
This study introduces ColonNeXt, a novel fully convolutional attention-based model for polyp segmentation from colonoscopy...
Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging
Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging
Malnutrition is a commonly observed side effect in cancer patients, with a 30–85% worldwide prevalence in this popul...
A Neural Network for Segmenting Tumours in Ultrasound Rectal Images
A Neural Network for Segmenting Tumours in Ultrasound Rectal Images
Ultrasound imaging is the most cost-effective approach for the early detection of rectal cancer, which is a high-risk canc...
Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer
Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer
Prostate cancer is one of the most prevalent male-specific diseases, where early and accurate diagnosis is essential for e...
Prediction of Alzheimer’s Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization
Prediction of Alzheimer’s Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization
Alzheimer’s disease is a degenerative neurological condition resulting in brain cell death and brain tissue loss. Mo...
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