Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability

Abstract

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces a groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian cancer, integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, and ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, and overall robust performance across diverse evaluation metrics. The incorporation of a stacked model further enhances overall performance. Experimental results, conducted on the UBC-OCEAN training and testing datasets, highlight the proficiency of ViT models in accurately classifying ovarian cancer subtypes based on histopathological images. ViT-Large-P32-384 stands out as a top performer, achieving 98.79% accuracy during training and 97.37% during testing. Visualizations, including Receiver Operating Characteristic (ROC) curves and Local Interpretable Model-agnostic Explanations (LIME), provide insights into discriminative capabilities and enhance model interpretability. The proposed CAD framework represents a significant advancement in ovarian cancer diagnostics, offering a promising avenue for accurate and transparent multi-class classification of histopathological images.