26 citations · 41 across the 5 of their papers we have counts for
8 papers
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…
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…
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…