Learning to Segment from Noisy Annotations: A Spatial Correction
  Approach
Jiachen Yao Yikai Zhang Songzhu Zheng Mayank Goswami Prateek Prasanna Chao Chen
Noisy labels can significantly affect the performance of deep neural networks
(DNNs). In medical image segmentation tasks, annotations are error-prone due to
the high demand in annotation time and in the annotators' expertise. Existing
methods mostly assume noisy labels in different pixels are \textit{i.i.d}.
However, segmentation label noise usually has strong spatial correlation and
has prominent bias in distribution. In this paper, we propose a novel Markov
model for segmentation noisy annotations that encodes both spatial correlation
and bias. Further, to mitigate such label noise, we propose a label correction
method to recover true label progressively. We provide theoretical guarantees
of the correctness of the proposed method. Experiments show that our approach
outperforms current state-of-the-art methods on both synthetic and real-world
noisy annotations.