activity
20192022
most citedMONAI: An open-source framework for deep learning in healthcare

452 citations · 461 across the 6 of their papers we have counts for

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Showing eess.IVShow all

7 papers · 1 filter

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…

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…