A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

no code implementations • 8 Feb 2019 • Onur Ozdemir, Rebecca L. Russell, Andrew A. Berlin

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments.

Decision Making General Classification +2

Automated Vulnerability Detection in Source Code Using Deep Representation Learning

2 code implementations • 11 Jul 2018 • Rebecca L. Russell, Louis Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, Marc W. McConley

The labeled dataset is available at: https://osf. io/d45bw/.

Representation Learning Vulnerability Detection

Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

no code implementations • NeurIPS 2018 • Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin

Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.

Code Repair Generative Adversarial Network +1

Automated software vulnerability detection with machine learning

no code implementations • 14 Feb 2018 • Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell, Onur Ozdemir, Leonard R. Kosta, Akshay Rangamani, Lei H. Hamilton, Gabriel I. Centeno, Jonathan R. Key, Paul M. Ellingwood, Erik Antelman, Alan Mackay, Marc W. McConley, Jeffrey M. Opper, Peter Chin, Tomo Lazovich

We then compare methods applied directly to source code with methods applied to artifacts extracted from the build process, finding that source-based models perform better.

BIG-bench Machine Learning Vulnerability Detection

Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection

no code implementations • 1 Dec 2017 • Onur Ozdemir, Benjamin Woodward, Andrew A. Berlin

Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture.

Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios

no code implementations • 4 Mar 2013 • Onur Ozdemir, Ruoyu Li, Pramod K. Varshney

The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR).

Classification General Classification