Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection

Deeksha Moodasarige Shama, Archana Venkataraman

Keywords: Aleatoric label noise, trustworthy AI, EEG, Epilepsy

Abstract:

"Supervised learning has become the dominant paradigm in
computer-aided diagnosis. Generally, these methods assume that the training labels represent “ground truth” information about the target phenomena. In actuality, the labels, often derived from human annotations, are noisy/unreliable. This aleatoric uncertainty poses significant challenges for modalities such as electroencephalography (EEG), in which “ground truth” is difficult to ascertain without invasive experiments. In this paper, we propose a novel Bayesian framework to mitigate the effects of aleatoric label uncertainty in the context of supervised deep learning. Our target application is EEG-based epileptic seizure detection. Our framework, called BUNDL, leverages domain knowledge to design a posterior distribution for the (unknown) “clean labels” that automatically adjusts based on the data uncertainty. Crucially, BUNDL can be wrapped around any existing detection model and trained using a novel KL divergence-based loss function. We validate BUNDL on both a simulated EEG dataset and the Temple University Hospital (TUH) corpus using three state-of-the-art deep networks. In all cases, integrating BUNDL improves the seizure detection performance. We also demonstrate that accounting for label noise using BUNDL improves seizure onset localization from EEG by reducing false predictions from artifacts"

Submission Number: 1