Abstract
Cervical cancer is one of the primary factors that endanger women’s health, and Thin-prep cytologic test (TCT) has been widely applied for early screening. Automatic whole slide image (WSI) classification is highly demanded, as it can significantly reduce the workload of pathologists. Current methods are mainly based on suspicious lesion patch extraction and classification, which ignore the intrinsic relationships between suspicious patches and neglect the other patches apart from the suspicious patches, and therefore limit their robustness and generalizability. Here we propose a novel method to solve the problem, which is based on graph attention network (GAT) and supervised contrastive learning. First, for each WSI, we extract and rank a large number of representative patches based on suspicious cell detection. Then, we select the top-K and bottom-K suspicious patches to construct two graphs seperately. Next, we introduce GAT to aggregate the features from each node, and use supervised contrastive learning to obtain valuable representations of graphs. Specifically, we design a novel contrastive loss so that the latent distances between two graphs are enlarged for positive WSIs and reduced for negative WSIs. Experimental results show that the proposed GAT method outperforms conventional methods, and also demonstrate the effectiveness of supervised contrastive learning.
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Zhang, X. et al. (2022). Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_20
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