Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout

• URL: http://arxiv.org/abs/2501.11258v1
• Date: Mon, 20 Jan 2025 03:54:30 GMT
• Title: Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
• Authors: Tal Zeevi, Lawrence H. Staib, John A. Onofrey,
• Abstract summary: Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal space, may fail to account for frequency-related noise common in medical imaging. A novel approach extends Dropout to the frequency domain, allowing attenuation of signal variations during inference.
• Score: 2.542402342792592
• License:
• Abstract: Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal space, may fail to account for frequency-related noise common in medical imaging, leading to biased predictive estimates. A novel approach extends Dropout to the frequency domain, allowing stochastic attenuation of signal frequencies during inference. This creates diverse global textural variations in feature maps while preserving structural integrity -- a factor we hypothesize and empirically show is contributing to accurately estimating uncertainties in semantic segmentation. We evaluated traditional MC-Dropout and the MC-frequency Dropout in three segmentation tasks involving different imaging modalities: (i) prostate zones in biparametric MRI, (ii) liver tumors in contrast-enhanced CT, and (iii) lungs in chest X-ray scans. Our results show that MC-Frequency Dropout improves calibration, convergence, and semantic uncertainty, thereby improving prediction scrutiny, boundary delineation, and has the potential to enhance medical decision-making.

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