activity
20192021
most citedConstrained Generative Adversarial Network Ensembles for Sharable Synthetic Data Generation

6 citations · 6 across the 4 of their papers we have counts for

collaborators

7 papers

eess.IV2021

Augmented Networks for Faster Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI

Engin Dikici, Xuan V. Nguyen, Matthew Bigelow +1

Early detection of brain metastases (BM) is one of the determining factors for the successful treatment of patients with cancer; however, the accurate detection of small BM lesions…

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…

eess.IV2019

Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?

Barbaros S. Erdal, Mutlu Demirer, Chiemezie C. Amadi +9

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodul…