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On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice

NeurIPS 2022 · Label-efficient and robust learning › Noisy labels · approach: Contrastive learning on noisy data

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Optimize Accuracy instead of DiceSegmentation under noisy labels · General marginal functionoptimal segmentation volumeMixed
Constrain feasible segmentations to the expected target volumeAccuracy and Dice optimization · Fixed-volume segmentationsoptimal segmentationNo clear effect
Optimize Accuracy or Dice with noisy marginal labels without a volume constraintOptimal segmentation · Gold Atlas and LIDC-IDRIvolume relative to expected target volume: 0.778; 1.267; 1.002; 1.432Mixed

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