Sungwon Ham
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View article: Optimized prediction of breast cancer tumor microenvironment using MRI-based intratumoral and peritumoral radiomics: a prospective study
Optimized prediction of breast cancer tumor microenvironment using MRI-based intratumoral and peritumoral radiomics: a prospective study Open
Objective The tumor microenvironment (TME), composed of non-tumor elements such as stromal matrix and immune cells, plays a critical role in tumor progression, metastasis, and treatment response. This study aimed to investigate the associa…
View article: Enhancing Lesion Detection in Rat CT Images: A Deep Learning-Based Super-Resolution Study
Enhancing Lesion Detection in Rat CT Images: A Deep Learning-Based Super-Resolution Study Open
Background/Objectives: Preclinical chest computed tomography (CT) imaging in small animals is often limited by low resolution due to scan time and dose constraints, which hinders accurate detection of subtle lesions. Traditional super-reso…
View article: Machine Learning–Based Prediction of Delayed Neurologic Sequelae in Carbon Monoxide Poisoning Using Automatically Extracted MR Imaging Features
Machine Learning–Based Prediction of Delayed Neurologic Sequelae in Carbon Monoxide Poisoning Using Automatically Extracted MR Imaging Features Open
Machine learning models developed using automatically extracted brain MRI features with clinical features can distinguish patients at delayed neurologic sequelae risk. The models enable effective prediction of delayed neurologic sequelae i…
View article: Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography
Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography Open
Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improv…
View article: Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study
Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study Open
Purpose To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience. Methods From October 2021 to November 2022, this p…
View article: Development of a Deep-Learning Model for Estimating Newborn Gestational Age via Lumbar Vertebral Segmentation on Plain Radiography
Development of a Deep-Learning Model for Estimating Newborn Gestational Age via Lumbar Vertebral Segmentation on Plain Radiography Open
Our deep learning model showed promising performance in lumbar vertebral body segmentation and GA estimation using plain radiographs, suggesting its potential utility as a supportive tool for neonatal maturity assessment in clinical practi…
View article: Development of a CT image-based virtual atelectasis simulation model and noninvasive lung nodule localization system
Development of a CT image-based virtual atelectasis simulation model and noninvasive lung nodule localization system Open
We successfully developed a simulation of lung atelectasis based on preoperative CT scans that closely resembled actual surgical videos. The integration of this presurgical atelectasis simulation is anticipated to enhance the accuracy of n…
View article: Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals
Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals Open
The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes diff…
View article: Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images
Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images Open
Semantic segmentation of breast and surrounding tissues in supine and prone breast magnetic resonance imaging (MRI) is required for various kinds of computer-assisted diagnoses for surgical applications. Variability of breast shape in supi…
View article: Fully automated condyle segmentation using 3D convolutional neural networks
Fully automated condyle segmentation using 3D convolutional neural networks Open
The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam compu…
View article: Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma Open
The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.
View article: Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status
Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status Open
We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous ce…
View article: Machine Learning Approach to Identify Stroke Within 4.5 Hours
Machine Learning Approach to Identify Stroke Within 4.5 Hours Open
Background and Purpose— We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging to identify patients with…
View article: 3D Cascaded U-Net with a Squeeze-and-Exicitation Block for Semantic Segmentation on Kidney and Renal Cell Carcinoma in Abdonimal Volumetric CT
3D Cascaded U-Net with a Squeeze-and-Exicitation Block for Semantic Segmentation on Kidney and Renal Cell Carcinoma in Abdonimal Volumetric CT Open
Segmentation is a fundamental process in medical image analysis. Recently, convolutional neural networks (CNNs) has allowed for automatic segmentation; however, segmentaiton of complex organs and diseases including the kidney or renal cell…