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20192024
most citedAre Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?

25 citations · 29 across the 6 of their papers we have counts for

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eess.IV2024

Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge

Kendall Schmidt, Benjamin Bearce, Ken Chang +21

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due t…

eess.IV2022★ 1 cited

A multi-reconstruction study of breast density estimation using Deep Learning

Vikash Gupta, Mutlu Demirer, Robert W. Maxwell +2

Breast density estimation is one of the key tasks in recognizing individuals predisposed to breast cancer. It is often challenging because of low contrast and fluctuations in mammo…

eess.IV2021★ 1 cited

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…

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.IV2019★ 25 cited

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…

eess.IV2019

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…