Evidential Deep Learning with Reweighted Margin Adjustment for Uncertainty-Driven Cervical OCT Image Diagnosis

Abstract

Cervical optical coherence tomography (OCT) is crucial in diagnosing cervical diseases due to its high-resolution imaging and non-invasive characteristics. However, recent improvements in classifying cervical OCT images have mainly focused on accuracy, often neglecting the importance of uncertainty and robustness in disease predictions. Uncertainty estimation faces challenges in collecting evidence for the minority classes, leading to fake confidence in evidential deep learning. There are similar issues with cervical OCT image classification. This study proposes a novel evidential learning process for cervical disease screening using OCT. It provides a measure of decision margin for each class with two novel mechanisms to address the hinge posted by class imbalance. Specifically, we introduce a reweighted margin adjustment strategy to learn less biased evidence among classes and make reliable uncertainty estimations. Extensive experiments on internal and external cervical OCT datasets demonstrate the proposed method outperforms competitive baseline methods, providing more reliable and robust predictions, particularly in out-of-distributions scenarios. By improving uncertainty estimation for class imbalance, our method offers valuable insights for practical clinical applications, highlighting the importance of measuring uncertainty in medical AI-aided diagnosis systems.