most citedLooking in the Right place for Anomalies: Explainable AI through Automatic Location Learning

15 citations · 15 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2020

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…

cs.CV202015 cited

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…

cs.CV2020

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…

eess.IV2020

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…

eess.IV2019

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…

cs.CV2019

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…