  Segmentation of pathological images is essential for accurate disease
diagnosis. The quality of manual labels plays a critical role in segmentation
accuracy; yet, in practice, the labels between pathologists could be
inconsistent, thus confusing the training process. In this work, we propose a
novel label re-weighting framework to account for the reliability of different
experts' labels on each pixel according to its surrounding features. We further
devise a new attention heatmap, which takes roughness as prior knowledge to
guide the model to focus on important regions. Our approach is evaluated on the
public Gleason 2019 datasets. The results show that our approach effectively
improves the model's robustness against noisy labels and outperforms
state-of-the-art approaches.
