Graph Segmentation-Based Pseudo-Labeling for Semi-Supervised Pathology Image Classification

Pathology image classification is an important step in cancer diagnosis and precision treatment. Training a pathology image classification model in a fully supervised manner requires exhaustive pixel-level manual annotations from pathologists, which may not be practical in real applications. Semi-supervised learning (SSL) has been widely used to exploit large amounts of unlabeled data to facilitate model training with a small set of labeled data. However, due to the limited annotations, it still suffers from the issue of inaccurate pseudo-labels of unlabeled data. In this paper, we propose a novel framework for semi-supervised pathology image classification, which incorporates graph-based segmentation to refine initial pseudo-labels of tissue regions by considering local and global contextual relationships of patches in whole-slide images (WSIs). Moreover, we define a new energy function for graph construction that allows the graph to take into account the uncertainty of network predictions on unlabeled data. Extensive experiments on two different pathology image datasets demonstrate the effectiveness of our method compared with state-of-the-art SSL baselines. In particular, when using 5% labeled data, our approach outperforms a strong baseline by 2.81% AUC.