134 citations · 170 across the 10 of their papers we have counts for
10 papers
Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chest
Mutlu Demirer, Richard D. White, Vikash Gupta +2
Background & Purpose: Chest X-Ray (CXR) use in pre-MRI safety screening for Lead-Less Implanted Electronic Devices (LLIEDs), easily overlooked or misidentified on a frontal view (o…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
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…
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…
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…
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…
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…
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…
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…
Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI
Engin Dikici, John L. Ryu, Mutlu Demirer +5
Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to preve…