activity
20192023
most citedMONAI: An open-source framework for deep learning in healthcare

452 citations · 495 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

eess.IV2020★ 2 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.IV2020★ 1 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.IV2020★ 6 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★ 1 cited

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…