Keywords: Diagnosis/Prediction, Cancer, Analysis/Processing, Robustness
Motivation: Motivated by the challenge of enhancing the robustness of deep neural network decisions against
variable noise in MRI-based brain tumor segmentation.
Goal(s): This study aims to evaluate the efficacy of probabilistic bottlenecks in enhancing segmentation robustness.
Approach: Our approach simulates structured perturbations at increasing strength
to assess their impact on segmentation performance utilizing the Wasserstein distance between
per-sample Dice score distributions and the sensitivity with respect to the perturbation strength.
Results: Results show probabilistic bottlenecks significantly increase robustness to Gaussian noise, yet
offer limited improvement towards Gaussian blur, with varying results for other perturbations,
highlighting the perturbation-specific nature of network resilience.
Impact: This study provides a tool to assess and guard against various perturbations in deep learning.It specifically demonstrates that probabilistic bottlenecks boost robustness of performance withrespect to certain noise types, but not all.
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