Dual consistency semi-supervised nuclei detection via global regularization and local adversarial learning

Abstract

Nuclei detection is a fundamental analytical step in digital histopathology image analysis. Since labeling the centroids for each nucleus in histopathology images is extremely time-consuming, researchers attempt to explore consistency-based approaches for efficient semi-supervised nuclei detection. However, existing methods can only be used for detection on small patches mostly containing one nucleus and contextual information among neighboring nuclei is not considered. On the contrary, using the whole image to achieve nuclei detection in a semi-supervised manner may suffer from a large amount of background noise and thus cannot yield optimal performance. To address these problems, we propose a novel semi-supervised learning method for nuclei detection on a full-size histopathology image via global consistency regularization and local consistency adversarial learning. Specifically, the proposed dual consistency semi-supervised learning can improve the efficiency in inference and learn the context-aware nuclei features by global consistency regularization. Meanwhile, local consistency adversarial learning is introduced to focus on nuclei regions and reinforce the local spatial contiguity of prediction maps. We have evaluated the proposed dual consistency semi-supervised method on public CRCHisto and collected SemiBCN datasets, and the results show that with the synergy of global consistency regularization and local consistency adversarial learning, our method delivers a significant improvement over the state-of-the-art methods.