207 citations · 321 across the 12 of their papers we have counts for
6 papers · 1 filter
3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient
Ken C. L. Wong, Mehdi Moradi
The convolutional layer and loss function are two fundamental components in deep learning. Because of the success of conventional deep learning kernels, the less versatile Gabor ke…
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Ken C. L. Wong, Elena S. Sinkovskaya, Alfred Z. Abuhamad +1
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in…
A robust network architecture to detect normal chest X-ray radiographs
Ken C. L. Wong, Mehdi Moradi, Joy Wu +10
We propose a novel deep neural network architecture for normalcy detection in chest X-ray images. This architecture treats the problem as fine-grained binary classification in whic…
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation
Ken C. L. Wong, Mehdi Moradi
Deep learning has largely reduced the need for manual feature selection in image segmentation. Nevertheless, network architecture optimization and hyperparameter tuning are mostly…
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Vaishnavi Subramanian, Hongzhi Wang, Joy T. Wu +3
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications. They are often described in radiology repor…
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
Alexandros Karargyris, Ken C. L. Wong, Joy T. Wu +2
In many screening applications, the primary goal of a radiologist or assisting artificial intelligence is to rule out certain findings. The classifiers built for such applications…