207 citations · 321 across the 12 of their papers we have counts for
15 papers
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…
Channel Scaling: A Scale-and-Select Approach for Transfer Learning
Ken C. L. Wong, Satyananda Kashyap, Mehdi Moradi
Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in…
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Tanveer Syeda-Mahmood, Ph. D, K. C. L Wong +6
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray d…
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Satyananda Kashyap, Alexandros Karargyris, Joy Wu +5
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses…
Chest X-ray Report Generation through Fine-Grained Label Learning
Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur +9
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the…
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…
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari +9
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning a…
Identifying disease-free chest X-ray images with deep transfer learning
Ken C. L. Wong, Mehdi Moradi, Joy Wu +1
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They are mostly used for screening, and an indication of disease typically results in subsequent test…
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
Ken C. L. Wong, Mehdi Moradi, Hui Tang +1
With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networ…
Building medical image classifiers with very limited data using segmentation networks
Ken C. L. Wong, Tanveer Syeda-Mahmood, Mehdi Moradi
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with…
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood +1
Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive…