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AI-Driven Radiology Report Generation for Traumatic Brain Injuries
AI-Driven Radiology Report Generation for Traumatic Brain Injuries
Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation ...
Automatic Identification of Adenoid Hypertrophy via Ensemble Deep Learning Models Employing X-ray Adenoid Images
Automatic Identification of Adenoid Hypertrophy via Ensemble Deep Learning Models Employing X-ray Adenoid Images
Adenoid hypertrophy, characterized by the abnormal enlargement of adenoid tissue, is a condition that can cause significan...
Denoising Multi-Level Cross-Attention and Contrastive Learning for Chest Radiology Report Generation
Denoising Multi-Level Cross-Attention and Contrastive Learning for Chest Radiology Report Generation
Chest radiology report generation plays a vital role in supporting diagnosis, alleviating physician workload, and reducing...
DICOM LUT is a Key Step in Medical Image Preprocessing Towards AI Generalizability
DICOM LUT is a Key Step in Medical Image Preprocessing Towards AI Generalizability
Image pre-processing has significant impact on performance of deep learning models in medicine; yet, there is no standardi...
Brain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans
Brain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans
Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has b...
Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT
Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed rad...
End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI
End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI
This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in o...
MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI
MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many ...
Medical Imaging Data Strategies for Catalyzing AI Medical Device Innovation
Medical Imaging Data Strategies for Catalyzing AI Medical Device Innovation
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) te...
Automated Integration of AI Results into Radiology Reports Using Common Data Elements
Automated Integration of AI Results into Radiology Reports Using Common Data Elements
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accurac...
Diagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review
Diagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review
Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload an...
Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis
Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT e...
In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify me...
Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation
Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation
Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models...
Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers
Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) ...
Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images
Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images
In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (F...
PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images
PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver condition characterized by excessive hepatic...
Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson’s Disease Stage Prediction
Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson’s Disease Stage Prediction
Parkinson’s disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functio...
A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation
A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation
Breast cancer remains a significant global health concern and is a leading cause of mortality among women. The accuracy of...
A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs
A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs
This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roo...
Semi-Supervised Medical Image Segmentation Based on Frequency Domain Aware Stable Consistency Regularization
Semi-Supervised Medical Image Segmentation Based on Frequency Domain Aware Stable Consistency Regularization
With the advancement of deep learning models nowadays, they have successfully applied in the semi-supervised medical image...
Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study
Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study
Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in pa...
Classification of Molecular Subtypes of Breast Cancer Using Radiomic Features of Preoperative Ultrasound Images
Classification of Molecular Subtypes of Breast Cancer Using Radiomic Features of Preoperative Ultrasound Images
Radiomics has been used as a non-invasive medical image analysis technique for diagnosis and prognosis prediction of breas...
Breast Cancer Histopathological Image Classification Based on Graph Assisted Global Reasoning
Breast Cancer Histopathological Image Classification Based on Graph Assisted Global Reasoning
Breast cancer ranks as the most prevalent cancer among women globally. Histopathological image analysis stands as one of t...
How to Efficiently Annotate Images for Best-Performing Deep Learning-Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
How to Efficiently Annotate Images for Best-Performing Deep Learning-Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, th...
Wound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning
Wound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning
Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image an...
Deep Learning-Based Multi-View Projection Synthesis Approach for Improving the Quality of Sparse-View CBCT in Image-Guided Radiotherapy
Deep Learning-Based Multi-View Projection Synthesis Approach for Improving the Quality of Sparse-View CBCT in Image-Guided Radiotherapy
While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced...
Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review
Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review
The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of C...
HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization
HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization
Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment...
InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images
InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images
Gastrointestinal (GI) disease examination presents significant challenges to doctors due to the intricate structure of the...
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