26 citations · 68 across the 15 of their papers we have counts for
18 papers
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…
Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study
Joy Tzung-yu Wu, Miguel Ángel Armengol de la Hoz, Po-Chih Kuo +15
The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2…
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…
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…
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…
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Edward T. Moseley, Joy T. Wu, Jonathan Welt +7
A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that ai…
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…
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…
Classification of radiology reports by modality and anatomy: A comparative study
Marina Bendersky, Joy Wu, Tanveer Syeda-Mahmood
Data labeling is currently a time-consuming task that often requires expert knowledge. In research settings, the availability of correctly labeled data is crucial to ensure that mo…
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Sebastian Gehrmann, Franck Dernoncourt, Yeran Li +8
Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classific…