DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning

no code implementations • 4 Jul 2019 • Jen-Tang Lu, Rupert Brooks, Stefan Hahn, Jin Chen, Varun Buch, Gopal Kotecha, Katherine P. Andriole, Brian Ghoshhajra, Joel Pinto, Paul Vozila, Mark Michalski, Neil A. Tenenholtz

We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection.

Specificity

4D CNN for semantic segmentation of cardiac volumetric sequences

no code implementations • 17 Jun 2019 • Andriy Myronenko, Dong Yang, Varun Buch, Daguang Xu, Alvin Ihsani, Sean Doyle, Mark Michalski, Neil Tenenholtz, Holger Roth

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.

Segmentation Semantic Segmentation

Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

no code implementations • 11 Aug 2018 • Christopher P. Bridge, Michael Rosenthal, Bradley Wright, Gopal Kotecha, Florian Fintelmann, Fabian Troschel, Nityanand Miskin, Khanant Desai, William Wrobel, Ana Babic, Natalia Khalaf, Lauren Brais, Marisa Welch, Caitlin Zellers, Neil Tenenholtz, Mark Michalski, Brian Wolpin, Katherine Andriole

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.

Segmentation

Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

no code implementations • 26 Jul 2018 • Hoo-chang Shin, Neil A. Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine Andriole, Mark Michalski

Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.

Data Augmentation Generative Adversarial Network +4