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Found 3 papers, 1 papers with code
Marginal Thresholding in Noisy Image Segmentation
Based on recent work on loss function characterization, it is shown that optimal solutions to soft-Dice can be recovered by thresholding solutions to cross-entropy with a particular a priori unknown threshold that efficiently can be computed.
Noisy Image Segmentation With Soft-Dice
This paper presents a study on the soft-Dice loss, one of the most popular loss functions in medical image segmentation, for situations where noise is present in target labels.
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy.
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