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20192021
most citedChest ImaGenome Dataset for Clinical Reasoning

26 citations · 41 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV202126 cited

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…

cs.CV2021

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…

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…

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…