Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels. (arXiv:2206.07994v1 [cs.CV])

Web: http://arxiv.org/abs/2206.07994

Noisy labels collected with limited annotation cost prevent medical image
segmentation algorithms from learning precise semantic correlations. Previous
segmentation arts of learning with noisy labels merely perform a pixel-wise
manner to preserve semantics, such as pixel-wise label correction, but neglect
the pair-wise manner. In fact, we observe that the pair-wise manner capturing
affinity relations between pixels can greatly reduce the label noise rate.
Motivated by this observation, we present a novel perspective for noisy
mitigation by incorporating both pixel-wise and …

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