6 papers
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers
Chace Ashcraft, Ted Staley, Josh Carney +4
Backdoor attacks, or trojans, pose a security risk by concealing undesirable behavior in deep neural network models. Open-source neural networks are downloaded from the internet da…
A Causal Framework for Aligning Image Quality Metrics and Deep Neural Network Robustness
Nathan Drenkow, Mathias Unberath
Image quality plays an important role in the performance of deep neural networks (DNNs) that have been widely shown to exhibit sensitivity to changes in imaging conditions. Convent…
Causality-Driven Audits of Model Robustness
Nathan Drenkow, William Paul, Chris Ribaudo +1
Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN perfo…
Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
Nathan Drenkow, Mitchell Pavlak, Keith Harrigian +5
Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparabl…
Investigating the Treacherous Turn in Deep Reinforcement Learning
Chace Ashcraft, Kiran Karra, Josh Carney +1
The Treacherous Turn refers to the scenario where an artificial intelligence (AI) agent subtly, and perhaps covertly, learns to perform a behavior that benefits itself but is deeme…
Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling
Benjamin D. Killeen, Bohua Wan, Aditya V. Kulkarni +4
Artificial intelligence (AI) is poised to transform healthcare by enabling personalized and efficient care through data-driven insights. Although radiology is at the forefront of A…