Whole slide cervical cancer classification via graph attention networks and contrastive learning

• Published in: Neurocomputing (Amsterdam) 2025-01, Vol.613, p.128787, Article 128787
• Main Authors: Fei, Manman, Zhang, Xin, Chen, Dongdong, Song, Zhiyun, Wang, Qian, Zhang, Lichi
• Format: Article
• Language: English
• Subjects: Cervical cancer; Graph attention network; Supervised contrastive learning; Whole slide image classification
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2024.128787

Cervical cancer is one of the most common cancers among women, which seriously threatens women’s health. Early screening can reduce the incidence rate and mortality. Thinprep cytologic test (TCT) is one of the important means of cytological screening, which has high sensitivity and specificity, and has been widely used in the early screening of cervical cancer. The automatic diagnosis of whole slide images (WSIs) by computers can effectively improve the efficiency and accuracy of doctors’ diagnoses. However, current methods ignore the intrinsic relationships between cervical cells in WSIs and neglect contextual information from the surrounding suspicious areas, and therefore limit their robustness and generalizability. In this paper, we propose a novel two-stage method to implement the automatic diagnosis of WSIs, which constructs Graph Attention Networks (GAT) based on local and global fields respectively to capture their contextual information in a hierarchical manner. In the first stage, we extract representative patches from each WSI through suspicious cell detection, and then employ a Local GAT to classify cervical cells by capturing correlations between suspicious cells in image tiles. This classification process provides the confidence and feature vectors for each suspicious cell. In the second stage, we perform WSI classification using a Global GAT model. We construct graphs corresponding to top-Kg and bottom-Kg cells for each WSI based on results from Local GAT, and introduce a supervised contrastive learning strategy to enhance the discriminative power of the extracted features. Experimental results demonstrate that our proposed method outperforms conventional approaches and effectively showcases the benefits of supervised contrastive learning. Our source code and example data are available at https://github.com/feimanman/Whole-Slide-Cervical-Cancer-Classification.