SCI Abstract

search
+
A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification
A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification
Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcom...
OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification
OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification
Early detection and accurate classification of retinal diseases, such as diabetic retinopathy (DR) and age-related macular...
A Comprehensive Guide to Selecting and (Potentially) Replacing PACS: Navigating the Decision-Making Processes
A Comprehensive Guide to Selecting and (Potentially) Replacing PACS: Navigating the Decision-Making Processes
Selecting a Picture Archiving and Communication System (PACS) is a strategic decision that impacts radiology workflow, com...
Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening
Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening
Glioma, pituitary tumors, and meningiomas constitute the major types of primary brain tumors. The challenge in achieving a...
MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique
MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique
Lung ultrasound (LUS) has been widely used by point-of-care systems in both children and adult populations to provide diff...
Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advan...
Generating Brain MRI with StyleGAN2-ADA: The Effect of the Training Set Size on the Quality of Synthetic Images
Generating Brain MRI with StyleGAN2-ADA: The Effect of the Training Set Size on the Quality of Synthetic Images
The potential of deep learning for medical imaging is often constrained by limited data availability. Generative models ca...
Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images
Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images
Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke e...
Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma
Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths, with accurate staging being critical ...
Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology
Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology
The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance ...
TCF-Net: A Hierarchical Transformer Convolution Fusion Network for Prostate Cancer Segmentation in Transrectal Ultrasound Images
TCF-Net: A Hierarchical Transformer Convolution Fusion Network for Prostate Cancer Segmentation in Transrectal Ultrasound Images
Accurate prostate segmentation from transrectal ultrasound (TRUS) images is the key to the computer-aided diagnosis of pro...
Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative
Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative
Osteoarthritis (OA) pain often does not correlate with magnetic resonance imaging (MRI)-detected structural abnormalities,...
A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach
A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach
Cystic bronchiectasis and pneumonia are respiratory conditions that significantly impact morbidity and mortality worldwide...
Facilitating Collaboration Across the Radiology Department by Expanding Access to Real-Time CT Protocols
Facilitating Collaboration Across the Radiology Department by Expanding Access to Real-Time CT Protocols
CT protocols can provide valuable information and guidance to radiologists, technologists, physicists, schedulers, and nur...
FairDITA: Disentangled Image-Text Alignment for Fair Skin Cancer Diagnosis
FairDITA: Disentangled Image-Text Alignment for Fair Skin Cancer Diagnosis
Recent advances in deep learning have significantly improved skin cancer classification, yet concerns regarding algorithmi...
Automatic Body Region Classification in CT Scans Using Deep Learning
Automatic Body Region Classification in CT Scans Using Deep Learning
Accurate classification of anatomical regions in computed tomography (CT) scans is essential for optimizing downstream dia...
A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods
A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods
Denoising Diffusion Probabilistic Models (DDPM) have emerged as a promising generative framework for sample synthesis, yet...
Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model
Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model
Radiology reports are essential for medical decision-making, providing crucial data for diagnosing diseases, devising trea...
Investigating the Effects of Image Scaling Techniques in Radiographic Measurements of Spinal Alignment and Motion: A Comparative Analysis
Investigating the Effects of Image Scaling Techniques in Radiographic Measurements of Spinal Alignment and Motion: A Comparative Analysis
Radiographic measurements from spinal radiographs are crucial in many diagnostic and therapeutic decisions. However, widel...
Fibrous Tissue Semantic Segmentation in CT Images of Diffuse Interstitial Lung Disease
Fibrous Tissue Semantic Segmentation in CT Images of Diffuse Interstitial Lung Disease
Interstitial-lung-disease progression assessment and diagnosis via radiological findings on computed tomography images req...
Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning
Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning
Fetal biometric assessments through ultrasound diagnostics are integral in obstetrics and gynecology, requiring considerab...
Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System
Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System
Accurate CT protocol assignment is crucial for optimizing medical imaging procedures. The integration of large language mo...
The Effect of Ultrasound Image Pre-Processing on Radiomics Feature Quality: A Study on Shoulder Ultrasound
The Effect of Ultrasound Image Pre-Processing on Radiomics Feature Quality: A Study on Shoulder Ultrasound
Radiomics, the extraction of quantitative features from medical images, has shown great promise in enhancing diagnostic an...
Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment using Classification and Segmentation
Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment using Classification and Segmentation
Prostate Cancer (PCa) is the second most common cancer in men and affects more than a million people each year. Grading pr...
Impact of MRI Texture Analysis on Complication Rate in MRI-Guided Liver Biopsies
Impact of MRI Texture Analysis on Complication Rate in MRI-Guided Liver Biopsies
Magnetic resonance imaging (MRI)–derived texture features are quantitative imaging parameters that may have valuable...
A Deep-Learning Approach for Vocal Fold Pose Estimation in Videoendoscopy
A Deep-Learning Approach for Vocal Fold Pose Estimation in Videoendoscopy
Accurate vocal fold (VF) pose estimation is crucial for diagnosing larynx diseases that can eventually lead to VF paralysi...
3D Wasserstein Generative Adversarial Network with Dense U-Net-Based Discriminator for Preclinical fMRI Denoising
3D Wasserstein Generative Adversarial Network with Dense U-Net-Based Discriminator for Preclinical fMRI Denoising
Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain funct...
Application of a Commercial Artificial Intelligence Software in Unilateral Mammography: Simulating Total Mastectomy Scenarios
Application of a Commercial Artificial Intelligence Software in Unilateral Mammography: Simulating Total Mastectomy Scenarios
This study was to evaluate the performance of commercially available artificial intelligence (AI) software in unilateral m...
Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images
Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images
This study aimed to develop a hybrid model combining radiomics and deep learning features derived from computed tomography...
DECODE-3DViz: Efficient WebGL-Based High-Fidelity Visualization of Large-Scale Images using Level of Detail and Data Chunk Streaming
DECODE-3DViz: Efficient WebGL-Based High-Fidelity Visualization of Large-Scale Images using Level of Detail and Data Chunk Streaming
The DECODE-3DViz pipeline represents a major advancement in the web-based visualization of large-scale medical imaging dat...
Load More
format_indent_increase