3 papers
cs.LG2026
Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation
David McShannon, Nicholas Dietrich
Physics-informed neural networks (PINNs) embed governing equations in their loss function, enabling mesh-free solutions to partial differential equations. Low training loss is trea…
cs.CV2026
Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Nicholas Dietrich, David McShannon
Introduction: Deep learning-based segmentation models are increasingly integrated into clinical imaging workflows, yet their robustness to adversarial perturbations remains incompl…
cs.SD2026
Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation
David McShannon, Anthony Mella, Nicholas Dietrich
Medical audio classification remains challenging due to low signal-to-noise ratios, subtle discriminative features, and substantial intra-class variability, often compounded by cla…