CAWM: Class-Aware Weight Map for Improved Semi-Supervised Nuclei Segmentation

Due to the rich histopathological information of nuclei in whole slide images, nuclei segmentation becomes essential for medical analysis. Since collecting sufficient pixel-wise annotations for supervised training of nuclei segmentation networks is challenging, semi-supervised nuclei segmentation methods have been extensively studied. In particular, many of them use pseudo-labels generated from unlabeled images for training the segmentation model. In this Letter, we propose a new pseudo-label handling method for semi-supervised nuclei segmentation. Specifically, based on our observation that nuclear features within the same image share high similarities, we define confidence maps for pseudo-labels and use them to adapt consistency regularization and contrastive loss measures. From extensive experiments on three public datasets, we demonstrate the effectiveness of the proposed method compared with other semi-supervised training methods.