134 citations · 240 across the 7 of their papers we have counts for
8 papers
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Varun Buch, Aoxiao Zhong, Xiang Li +8
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hosp…
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
Aoxiao Zhong, Xiang Li, Dufan Wu +17
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public…
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…
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…
An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
Charles Lu, Julia Strout, Romane Gauriau +4
Healthcare is one of the most promising areas for machine learning models to make a positive impact. However, successful adoption of AI-based systems in healthcare depends on engag…
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports
Neil Deshmukh, Selin Gumustop, Romane Gauriau +6
Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning appr…
DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
Jen-Tang Lu, Rupert Brooks, Stefan Hahn +9
We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in…
4D CNN for semantic segmentation of cardiac volumetric sequences
Andriy Myronenko, Dong Yang, Varun Buch +6
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a sm…