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Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study
Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study
This study aims to investigate the feasibility, usability, and effectiveness of a Retrieval-Augmented Generation (RAG)-pow...
Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions
Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions
Brain tumor detection from medical images, especially magnetic resonance imaging (MRI) scans, is a critical task in early ...
A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images
A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the developme...
Highly Accurate Occupational Pneumoconiosis Staging via Dark Channel Prior-Inspired Lesion Area Enhancement
Highly Accurate Occupational Pneumoconiosis Staging via Dark Channel Prior-Inspired Lesion Area Enhancement
Occupational pneumoconiosis (OP) staging is the core for OP diagnosis. It is essentially an image classification task conc...
Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study
Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study
To explore the diagnostic value of deep learning (DL) imaging based on MRI in predicting placenta accreta spectrum (PAS) i...
Machine learning-based model assists in differentiating Complex Pulmonary Disease from Pulmonary Tuberculosis: A Multicenter Study
Machine learning-based model assists in differentiating Complex Pulmonary Disease from Pulmonary Tuberculosis: A Multicenter Study
The number of Mycobacterium avium-intracellulare complex pulmonary disease patients is increasing globally. Distinguishing...
New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification
New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification
Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands ...
Breast Ultrasound Image Segmentation Using Multi-branch Skip Connection Search
Breast Ultrasound Image Segmentation Using Multi-branch Skip Connection Search
To reduce the cost of designing neural networks and improve the accuracy of breast ultrasound image segmentation, an encod...
Prediction of Future Risk of Moderate to Severe Kidney Function Loss Using a Deep Learning Model–Enabled Chest Radiography
Prediction of Future Risk of Moderate to Severe Kidney Function Loss Using a Deep Learning Model–Enabled Chest Radiography
Chronic kidney disease (CKD) remains a major public health concern, requiring better predictive models for early intervent...
Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies
Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies
Diabetic retinopathy (DR) is a significant vision-threatening condition, necessitating accurate and efficient automated sc...
Evaluating the Impact of a Ki-67 Decision Support Algorithm on Pathology Residents’ Scoring Accuracy
Evaluating the Impact of a Ki-67 Decision Support Algorithm on Pathology Residents’ Scoring Accuracy
Ki-67 scoring is of essential importance in the evaluation of breast cancer. We evaluated a Ki-67 algorithm as a decision ...
Enhancing Radiology Clinical Histories Through Transformer-Based Automated Clinical Note Summarization
Enhancing Radiology Clinical Histories Through Transformer-Based Automated Clinical Note Summarization
Insufficient clinical information provided in radiology requests, coupled with the cumbersome nature of electronic health ...
Enhancing Burn Diagnosis through SE-ResNet18 and Confidence Filtering
Enhancing Burn Diagnosis through SE-ResNet18 and Confidence Filtering
Accurate classification of burn severity is crucial for effective clinical treatment; however, existing methods often fail...
TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images
TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images
Accurately diagnosing various types of breast lesions is critical for assessing breast cancer risk and predicting patient ...
Development and Validation of an Early Recurrence Prediction Model for High-Grade Glioma Integrating Temporalis Muscle and Tumor Features: Exploring the Prognostic Value of Temporalis Muscle
Development and Validation of an Early Recurrence Prediction Model for High-Grade Glioma Integrating Temporalis Muscle and Tumor Features: Exploring the Prognostic Value of Temporalis Muscle
This study aimed to develop and validate a predictive model for early recurrence of high-grade glioma (HGG) within 180&...
Improved biometric quantification in 3D ultrasound biomicroscopy via generative adversarial networks-based image enhancement
Improved biometric quantification in 3D ultrasound biomicroscopy via generative adversarial networks-based image enhancement
This study addresses the limitations of inexpensive, high-frequency ultrasound biomicroscopy (UBM) systems in visualizing ...
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence o...
Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports
Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports
In current radiology practice, radiologists identify a finding in the current imaging exam, manually match it against the ...
Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review
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 serious health issues s...
A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification
A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification
The aim of this study is to investigate the role of [18F]-PSMA-1007 PET in differentiating high- and low-risk prostate can...
MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features
MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features
The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces ...
Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images
Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images
Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual t...
A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification
A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification
Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness an...
Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images
Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images
Predicting long-term clinical outcomes based on the early DSC PWI MRI scan is valuable for prognostication, resource manag...
Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification
Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification
Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trust...
Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts
Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts
Ischemic changes are not visible on non-contrast head CT until several hours after infarction, though deep convolutional n...
Empowering Women in Imaging Informatics: Confronting Imposter Syndrome, Addressing Microaggressions, and Striving for Work-Life Harmony
Empowering Women in Imaging Informatics: Confronting Imposter Syndrome, Addressing Microaggressions, and Striving for Work-Life Harmony
For the past 6 years, the Society for Imaging Informatics in Medicine (SIIM) annual meeting has provided a forum ...
Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study
Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study
The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preop...
Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer
Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer
Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagno...
Deep Learning–Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors
Deep Learning–Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors
Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector dis...
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