Effect of domain-specific self-supervised pretraining on predictive uncertainty for colorectal polyp characterization

Abstract

Colorectal polyps (CRPs) are potential precursors of colorectal cancer (CRC), one of the most common types of cancer worldwide. Computer-Aided Diagnosis (CADx) systems can play a crucial role as a second opinion for endoscopists in characterizing CRPs and contribute to the diagnostic performance of colonoscopies. Despite their potential, deep neural network-based systems often tend to overestimate the confidence about their decisions and provide predictive probabilities that are poorly related to their classification accuracy. Quantifying uncertainty of such supportive systems is crucial for optimal clinical workflow integration and physician's acceptance. Thus, a trustworthy CADx system is expected to provide accurate and well-calibrated classification confidence. Transfer learning from either natural image datasets, such as ImageNet, or other datasets with similar modalities, has been widely used for improving the accuracy of deep learning-based systems in medical image classification. In this paper, we study the impact of domain-specific pretraining on the calibration and the overall performance of a CADx system for CRP characterization. We evaluate our hypothesis on a fully deterministic and a hybrid Bayesian version of each approach using a generic ResNet50 architecture. Experimental results demonstrate the effectiveness of domain-specific pretraining in achieving a higher overall characterization AUC. Additionally, the in-domain and out-of-domain pretrained models portray similar calibration error rates, however, their corresponding hybrid Bayesian models offer higher robustness with improved calibration performance. A hybrid Bayesian version of a domain-specific pretraining approach has shown to significantly improve the accuracy and reliability of CADx systems used for CRP characterization and similar positive effects may be expected for other medical imaging applications.