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.
Automatic brain tumor segmentation us- ing convolutional neural networks with test-time aug- mentation,
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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.