Multiview clustering with graph autoencoder for reconstructing histopathological images in oral cancer

Resumo

Objective: Early detection is critical for accurately diagnosing and effectively treating oral squamous cell carcinoma, particularly in regions like Southeast Asia where the prevalence is high. Multiview clustering and graph autoencoders (GAEs) hold promise for enhancing classification and diagnostic accuracy in oral cancer histopathological images. This study explores multiview clustering with graph autoencoders (MCGAE) for reconstructing and analyzing histopathological images in oral cancer. Material and Methods: The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma Collection serves as a comprehensive dataset, encompassing histopathological images from 756 head and neck squamous cell carcinoma samples. Image preprocessing involves resizing to preserve critical features, feature extraction using pre-trained deep learning architectures, and multiview clustering with GAEs to enhance clustering performance by integrating data from various views. The training process optimizes the model using reconstruction loss, clustering loss, and contrastive loss, achieving convergence when the total loss stabilizes after 100 epochs. Clustering analysis of the dataset reveals strong separation between clusters, as evidenced by high Calinski-Harabasz and Davies-Bouldin scores. Results: The model’s performance, enhanced by MCGAE embeddings, is demonstrated through higher silhouette scores and a superior Calinski-Harabasz Index. The MCGAE model achieves an accuracy of 93.5%, an F1 score of 89.36%, and an average precision of 97.32%. Furthermore, the low Mean Squared Error and high R2 score underscore the model’s reliability and effectiveness in striking a balance between precision and recall. Conclusion: Multiview GAEs enhance histopathological diagnoses by reducing diagnostic errors and variability, promoting continuous learning, and streamlining diagnostic workflows.

KEYWORDS

Deep learning; Histopathology; Multiview clustering; Oral cancer; Synthetic histopathological images.