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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...
NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma
NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma
To evaluate the prognostic significance and molecular mechanism of NETosis markers in ovarian serous cystadenocarcinoma (O...
Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study
Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study
Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluatio...
Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications
Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications
Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabl...
CACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label
CACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label
Circulating genetically abnormal cells (CACs) serve as crucial biomarkers for lung cancer diagnosis. Detecting CACs holds ...
Diagnosing Respiratory Variability: Convolutional Neural Networks for Chest X-ray Classification Across Diverse Pulmonary Conditions
Diagnosing Respiratory Variability: Convolutional Neural Networks for Chest X-ray Classification Across Diverse Pulmonary Conditions
The global burden of lung diseases is a pressing issue, particularly in developing nations with limited healthcare access....
Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning
Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning
Periapical index (PAI) scoring system is the most popular index for evaluating apical periodontitis (AP) on radiographs. I...
Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model
Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model
The aim of this study is to develop a fine-tuned large language model that classifies interventional radiology reports int...
Semi-supervised Ensemble Learning for Automatic Interpretation of Lung Ultrasound Videos
Semi-supervised Ensemble Learning for Automatic Interpretation of Lung Ultrasound Videos
Point-of-care ultrasound (POCUS) stands as a safe, portable, and cost-effective imaging modality for swift bedside patient...
Correction to: The FIND Program: Improving Follow-up of Incidental Imaging Findings
Correction to: The FIND Program: Improving Follow-up of Incidental Imaging Findings
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, whi...
Journal of Imaging Informatics in Medicine
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Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms
Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms
Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patien...
Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks
Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks
Two data-driven algorithms were developed for detecting and characterizing Inferior Vena Cava (IVC) filters on abdominal c...
Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review
Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review
With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets ha...
DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation
DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation
Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. T...
External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer
External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer
Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop pred...
Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios
Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios
Deep neural networks (DNNs) have already impacted the field of medicine in data analysis, classification, and image proces...
Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis—a Pilot Study
Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis—a Pilot Study
The purpose of this study is to evaluate the accuracy and inter-observer agreement of a quantitative pulmonary surface irr...
Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment
Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment
Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-s...
Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7
Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7
The study aimed to evaluate the impact of image size, area of detection (IoU) thresholds and confidence thresholds on the ...
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
Large numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training...
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