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Noisy labels can significantly affect the performance of deep neural networks(DNNs). In medical image segmentation tasks, annotations are error-prone due tothe high demand in annotation time and in the annotators' expertise. Existingmethods mostly assume noisy labels in different pixels are \textit{i.i.d}.However, segmentation label noise usually has strong spatial correlation andhas prominent bias in distribution. In this paper, we propose a novel Markovmodel for segmentation noisy annotations that encodes both spatial correlationand bias. Further, to mitigate such label noise, we propose a label correctionmethod to recover true label progressively. We provide theoretical guaranteesof the correctness of the proposed method. Experiments show that our approachoutperforms current state-of-the-art methods on both synthetic and real-worldnoisy annotations.

Learning to Segment from Noisy Annotations: A Spatial Correction Approach