Monte-Carlo Frequency Dropout, which randomly removes frequency components in feature maps, yields better-calibrated uncertainty estimates than standard signal dropout for medical image segmentation.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
Monte-Carlo Frequency Dropout, which randomly removes frequency components in feature maps, yields better-calibrated uncertainty estimates than standard signal dropout for medical image segmentation.