most citedConstrained Generative Adversarial Network Ensembles for Sharable Synthetic Data Generation

6 citations · 9 across the 5 of their papers we have counts for

collaborators

7 papers

eess.IV20202 cited

Deep Learning-Based Automatic Detection of Poorly Positioned Mammograms to Minimize Patient Return Visits for Repeat Imaging: A Real-World Application

Vikash Gupta, Clayton Taylor, Sarah Bonnet +5

Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In order to maxi…

eess.IV20201 cited

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning

Vikash Gupta1, Holger Roth, Varun Buch3 +9

The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial i…

eess.IV2020

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis during CCTA Evaluation of Chest-Pain in the Emergency Department: Preparing an Application for Real-World Use

Richard D. White, Barbaros S. Erdal, Mutlu Demirer +9

Coronary Computed Tomography Angiography (CCTA) evaluation of chest-pain patients in an Emergency Department (ED) is considered appropriate. While a negative CCTA interpretation su…

eess.IV20206 cited

Constrained Generative Adversarial Network Ensembles for Sharable Synthetic Data Generation

Engin Dikici, Luciano M. Prevedello, Matthew Bigelow +2

The sharing of medical imaging datasets between institutions, and even inside the same institution, is limited by various regulations/legal barriers. Although these limitations are…

q-bio.QM2020

Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis

Sema Candemir, Xuan V. Nguyen, Luciano M. Prevedello +3

Purpose: This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the cl…

eess.IV2019

Integrating AI into Radiology workflow: Levels of research, production, and feedback maturity

Engin Dikici, Matthew Bigelow, Luciano M. Prevedello +2

This report represents a roadmap for integrating Artificial Intelligence (AI)-based image analysis algorithms into existing Radiology workflows such that: (1) radiologists can sign…