3 citations · 4 across the 4 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
Nathan Drenkow, Alvin Tan, Chace Ashcraft +1
The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision mod…
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
Zhaoshuo Li, Nathan Drenkow, Hao Ding +5
Scene depth estimation from stereo and monocular imagery is critical for extracting 3D information for downstream tasks such as scene understanding. Recently, learning-based method…
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…