  Automatic histopathology image segmentation is crucial to disease analysis.
Limited available labeled data hinders the generalizability of trained models
under the fully supervised setting. Semi-supervised learning (SSL) based on
generative methods has been proven to be effective in utilizing diverse image
characteristics. However, it has not been well explored what kinds of generated
images would be more useful for model training and how to use such images. In
this paper, we propose a new data guided generative method for histopathology
image segmentation by leveraging the unlabeled data distributions. First, we
design an image generation module. Image content and style are disentangled and
embedded in a clustering-friendly space to utilize their distributions. New
images are synthesized by sampling and cross-combining contents and styles.
Second, we devise an effective data selection policy for judiciously sampling
the generated images: (1) to make the generated training set better cover the
dataset, the clusters that are underrepresented in the original training set
are covered more; (2) to make the training process more effective, we identify
and oversample the images of "hard cases" in the data for which annotated
training data may be scarce. Our method is evaluated on glands and nuclei
datasets. We show that under both the inductive and transductive settings, our
SSL method consistently boosts the performance of common segmentation models
and attains state-of-the-art results.
