Learning with Noise: Mask-Guided Attention Model for Weakly Supervised Nuclei Segmentation

Deep convolutional neural networks have been highly effective in segmentation tasks. However, high performance often requires large datasets with high-quality annotations, especially for segmentation, which requires precise pixel-wise labelling. The difficulty of generating high-quality datasets often constrains the improvement of research in such areas. To alleviate this issue, we propose a weakly supervised learning method for nuclei segmentation that only requires annotation of the nuclear centroid. To train the segmentation model with point annotations, we first generate boundary and superpixel-based masks as pseudo ground truth labels to train a segmentation network that is enhanced by a mask-guided attention auxiliary network. Then to further improve the accuracy of supervision, we apply Confident Learning to correct the pseudo labels at the pixel-level for a refined training. Our method shows highly competitive performance of cell nuclei segmentation in histopathology images on two public datasets. Our code is available at: https://github.com/RuoyuGuo/MaskGA_Net.

Automated summary

A weakly supervised learning method for nuclei segmentation that only requires annotation of the nuclear centroid was proposed, which utilized a mask-guided attention auxiliary network and Confident Learning to improve pixel-level labels, achieving highly competitive performance on two public datasets for cell nuclei segmentation.