4 papers
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…
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…