Ehwa Yang
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View article: Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients
Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients Open
We aimed to develop efficient data labeling strategies for ground truth segmentation in lower-leg magnetic resonance imaging (MRI) of patients with Charcot-Marie-Tooth disease (CMT) and to develop an automated muscle segmentation model usi…
View article: Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging
Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging Open
In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarker…
View article: Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI
Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI Open
Background: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). Methods: DWI at b-values of 0, 10…
View article: Predicting Lung Cancer Survival after Curative Surgery Using Deep Learning of Diffusion MRI
Predicting Lung Cancer Survival after Curative Surgery Using Deep Learning of Diffusion MRI Open
The survival of lung cancer patients is expected differently according to the stage at diagnosis. However, each individual patient experiences different survival results even in the same stage group. DWI and ADC are two of widely used prog…
View article: Brain signatures based on structural <scp>MRI</scp>: Classification for <scp>MCI</scp>, <scp>PMCI</scp>, and <scp>AD</scp>
Brain signatures based on structural <span>MRI</span>: Classification for <span>MCI</span>, <span>PMCI</span>, and <span>AD</span> Open
Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging da…
View article: Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network
Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network Open
In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates …
View article: Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge
Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge Open
Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a h…
View article: Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning
Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning Open
Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW…
View article: Automatic segmentation of kidney and kidney tumors using the cascaded dense network combined with cLSTM in CT scan
Automatic segmentation of kidney and kidney tumors using the cascaded dense network combined with cLSTM in CT scan Open
In this study, we develop the cascaded deep neural network model for automatic segmentation of the kidney and kidney tumors in CT scans. We used the fully dense network (to extract inner-slice image features) combined with bi-cLSTM (to ext…
View article: Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning
Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning Open
Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applicat…