207 citations · 349 across the 16 of their papers we have counts for
20 papers
Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms
Ashutosh Jadhav, Satyananda Kashyap, Hakan Bulu +6
Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could…
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
Gaurang Karwande, Amarachi Mbakawe, Joy T. Wu +3
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received…
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…
Chest ImaGenome Dataset for Clinical Reasoning
Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou +9
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is…
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Nkechinyere N. Agu, Joy T. Wu, Hanqing Chao +5
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look a…
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…
Statistical learning and cross-validation for point processes
Ottmar Cronie, Mehdi Moradi, Christophe A. N. Biscio
This paper presents the first general (supervised) statistical learning framework for point processes in general spaces. Our approach is based on the combination of two new concept…
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development
Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou +8
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye tracking system while a radiologist…
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu +2
Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level rad…
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…
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…
Age prediction using a large chest X-ray dataset
Alexandros Karargyris, Satyananda Kashyap, Joy T Wu +3
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a person…
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…
Bimodal network architectures for automatic generation of image annotation from text
Mehdi Moradi, Ali Madani, Yaniv Gur +2
Medical image analysis practitioners have embraced big data methodologies. This has created a need for large annotated datasets. The source of big data is typically large image col…
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…