9 citations · 9 across the 1 of their papers we have counts for
5 papers
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…
Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
Christopher P. Bridge, Michael Rosenthal, Bradley Wright +15
The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard f…
DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning
Jen-Tang Lu, Stefano Pedemonte, Bernardo Bizzo +5
The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most spe…
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers +5
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces…