Uncertainty Quantification in DL Models for Cervical Cytology

Shubham Ojha, Aditya Narendra

Keywords: Deep learning, Uncertainty, Digital Cytopathalogy, Cervical Cancer

Abstract: Deep Learning (DL) has demonstrated significant promise in digital pathological applications both histopathology and cytopathology. However, the majority of these works primarily concentrate on evaluating the general performance of the models and overlook the crucial requirement for uncertainty quantification which is necessary for real-world clinical application. In this study, we examine the change in predictive performance and the identification of mispredictions through the incorporation of uncertainty estimates for DL-based Cervical cancer classification. Specifically, we evaluate the efficacy of three methods—Monte Carlo(MC) Dropout, Ensemble Method, and Test Time Augmentation(TTA) using three metrics: variance, entropy, and sample mean uncertainty. The results demonstrate that integrating uncertainty estimates improves the model’s predictive capacity in high-confidence regions, while also serving as an indicator for the model’s mispredictions in low-confidence regions.

Submission Number: 124