SCI Abstract

search
+
SwinPix: A Swin Transformer-based Pix2Pix Framework for Low-Dose PET Denoising Using Multi-level Inputs Toward Standard-Dose Quality
SwinPix: A Swin Transformer-based Pix2Pix Framework for Low-Dose PET Denoising Using Multi-level Inputs Toward Standard-Dose Quality
This study introduces SwinPix, a novel network architecture designed to explore the effectiveness of multi-level low-dose ...
LRF-UNet: Low-Rank Factorized Convolution Deep-Learning Networks for Visceral Adipose and Muscle Tissue Segmentation in Abdominal Computed Tomography Image
LRF-UNet: Low-Rank Factorized Convolution Deep-Learning Networks for Visceral Adipose and Muscle Tissue Segmentation in Abdominal Computed Tomography Image
Quantitative analysis of skeletal muscle (SM) and visceral adipose tissue (VAT) cross-sectional volumes at the third lumba...
The Emory Knee Radiograph (MRKR) Dataset
The Emory Knee Radiograph (MRKR) Dataset
The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 8...
MDE-YOLO: Edge-Aware Multi-Scale Fusion Lightweight High-Precision Model for Cervical Cell Detection
MDE-YOLO: Edge-Aware Multi-Scale Fusion Lightweight High-Precision Model for Cervical Cell Detection
Cervical cancer is one of the most prevalent cancers among women, and early diagnosis is crucial for improving treatment o...
Stenosis-YOLO: Semisupervised YOLOv8 with Edge Enhancement and Hierarchical Feature Fusion for Coronary Stenosis Segmentation
Stenosis-YOLO: Semisupervised YOLOv8 with Edge Enhancement and Hierarchical Feature Fusion for Coronary Stenosis Segmentation
An early and accurate diagnosis of coronary artery disease (CAD) is essential for effective treatment. Although X-ray coro...
Task-Based Sampling of Patient Data for Rigorous Machine Learning/AI Performance Assessment
Task-Based Sampling of Patient Data for Rigorous Machine Learning/AI Performance Assessment
To assess the performance of an AI algorithm, an independent dataset is needed that matches the intended clinical claim an...
Advanced Prompting Techniques Informed by Clinical Expertise Improve the Accuracy of LLM Data Extraction but Increase Non-Determinism
Advanced Prompting Techniques Informed by Clinical Expertise Improve the Accuracy of LLM Data Extraction but Increase Non-Determinism
Prompt engineering techniques which aid in the use of generative artificial intelligence to address classification tasks h...
MLB-Net: A Multi-level Lesion-Aware and Boundary-Enhanced Network for Polyp Segmentation
MLB-Net: A Multi-level Lesion-Aware and Boundary-Enhanced Network for Polyp Segmentation
Accurate automatic polyp segmentation is crucial for the early diagnosis and treatment of colorectal cancer. However, the ...
CONReg: Uncertainty-Aware Medical Image Registration Using Conformal Prediction
CONReg: Uncertainty-Aware Medical Image Registration Using Conformal Prediction
Deep learning (DL) has advanced medical image registration, but most models produce only point estimates of dense displace...
A Dual-Reweighting Defense Strategy Against Data Poisoning Attacks in Medical Image Classification Models
A Dual-Reweighting Defense Strategy Against Data Poisoning Attacks in Medical Image Classification Models
With the rapid advancement of deep learning models in disease detection and medical image analysis, concerns regarding the...
The Development and External Testing of Deep Learning Model for Automated Classification of Intervertebral Disc Degeneration on CT Images
The Development and External Testing of Deep Learning Model for Automated Classification of Intervertebral Disc Degeneration on CT Images
The study aims to develop and externally test a deep learning (DL) model using CT images for automatic classification of i...
CALM-RAD: Calibrated GPT-4o Confidence-Based Triage Enables 96–100% Accuracy in Automated TNM Staging of Head and Neck Cancer Reports
CALM-RAD: Calibrated GPT-4o Confidence-Based Triage Enables 96–100% Accuracy in Automated TNM Staging of Head and Neck Cancer Reports
The purpose of this study is to assess the CALM-RAD framework that converts GPT-4o token-level log-probabilities (TLPs) in...
EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
Lung cancer is a leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and poor early de...
Exploration of Deep Learning Methods for Synthetic T2-Weighted Pelvic MRI Generation from CT Scans: A Technical Feasibility Study
Exploration of Deep Learning Methods for Synthetic T2-Weighted Pelvic MRI Generation from CT Scans: A Technical Feasibility Study
Synthesizing T2-weighted MRI from CT scans presents a challenging ill-posed problem that remains underexplored in abdomino...
Adaptive, Privacy-Preserving Small Language Models for Multi-Task Clinical Assistance
Adaptive, Privacy-Preserving Small Language Models for Multi-Task Clinical Assistance
The purpose of this study is to evaluate whether a single, fine-tuned SLM can match or exceed the performance of LLMs acro...
Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation
Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation
Radiological protocol selection is a critical but time-consuming step in clinical workflow, requiring radiologists to matc...
Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier
Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier
Deep learning for invasive lung adenocarcinoma subtyping remains vulnerable to real-world imaging perturbations. We presen...
Out-of-Distribution Detection in Medical Image Segmentation with $$\beta $$-VAE and Likelihood Regret
Out-of-Distribution Detection in Medical Image Segmentation with $$\beta $$-VAE and Likelihood Regret
The performance of deep learning models can be compromised in the presence of Out-of-Distribution (OOD) data, i.e., data w...
Compact Involutional Transformer for Automated Detection of Pediatric Tooth Number Anomalies on Panoramic Radiographs
Compact Involutional Transformer for Automated Detection of Pediatric Tooth Number Anomalies on Panoramic Radiographs
Pediatric tooth number anomalies can compromise occlusion, craniofacial development, and long-term treatment planning. Thi...
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...
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...
Digital Twin Technology In Radiology
Digital Twin Technology In Radiology
A digital twin is a computational model that provides a virtual representation of a specific physical object, system, or p...
Assessment of Lower Limb Muscle Volume Using 3D Ultrasonography: Validity and Reliability Compared to MRI
Assessment of Lower Limb Muscle Volume Using 3D Ultrasonography: Validity and Reliability Compared to MRI
Muscle volume is a key indicator of strength and neuromuscular health, commonly assessed via Magnetic Resonance Imaging (M...
Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification
Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification
Colorectal cancer (CRC) remains one of the most common and lethal malignancies worldwide. The current gold standard for CR...
In Silico Digital Breast Tomosynthesis Dataset for the Comparative Analysis of Deep Learning Models in Tumor Segmentation
In Silico Digital Breast Tomosynthesis Dataset for the Comparative Analysis of Deep Learning Models in Tumor Segmentation
The scarcity of publicly available digital breast tomosynthesis (DBT) datasets significantly limits the development of rob...
Protecting Radiology Data and Devices Against Cybersecurity Threats: A Joint White Paper of the American College of Radiology (ACR) and Society for Imaging Informatics in Medicine (SIIM)
Protecting Radiology Data and Devices Against Cybersecurity Threats: A Joint White Paper of the American College of Radiology (ACR) and Society for Imaging Informatics in Medicine (SIIM)
Modern radiology relies entirely on digital imaging systems. Healthcare records draw cybercriminals’ attention. On-s...
A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment
A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment
Hand-wrist radiographs are used in bone age prediction. Computer-assisted clinical decision support systems offer solution...
Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study
Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study
We developed an MRI-based habitat radiomics model (HRM) to predict p53-abnormal (p53abn) molecular subtypes of endometrial...
Can Machine Learning Predict Metastatic Sites in Pancreatic Ductal Adenocarcinoma? A Radiomic Analysis
Can Machine Learning Predict Metastatic Sites in Pancreatic Ductal Adenocarcinoma? A Radiomic Analysis
Pancreatic ductal adenocarcinoma (PDAC) exhibits high metastatic potential, with distinct prognoses based on metastatic si...
Evaluation of Net Withdrawal Time and Colonoscopy Video Summarization Using Deep Learning Based Automated Temporal Video Segmentation
Evaluation of Net Withdrawal Time and Colonoscopy Video Summarization Using Deep Learning Based Automated Temporal Video Segmentation
Adequate withdrawal time is crucial in colonoscopy, as it is directly associated with polyp detection rates. However, trad...
Load More
format_indent_increase