Uncertainty-informed detection of epileptogenic brain malformations using bayesian neural networks

Abstract

Focal cortical dysplasia (FCD) is a prevalent surgically-amenable epileptogenic malformation of cortical development. On MRI, FCD typically presents with cortical thickening, hyperintensity, and blurring of the gray-white matter interface. These changes may be visible to the naked eye, or subtle and be easily overlooked. Despite advances in MRI analytics, current machine learning algorithms fail to detect FCD in up to 50% of cases. Moreover, the deterministic nature of current algorithms does not allow conducting risk assessments of such predictions, an essential step in clinical decision-making. Here, we propose an algorithm formulated on Bayesian convolutional neural networks (CNN) providing information on prediction uncertainty, while leveraging this information to improve classification performance. Our classifier was trained on a patch-based augmented dataset derived from 56 patients with histologically-validated FCD to distinguish the lesion from healthy tissue. The algorithm was trained and cross-validated on multimodal 3T MRI data. Compared to a non-Bayesian learner with the same network architecture and complexity, the uncertainty-informed Bayesian CNN classifiers showed significant improvement in sensitivity (89% vs 82%; p < 0.05) while specificity was high for both classifiers. We demonstrate empirically the effectiveness of our uncertainty-informed CNN algorithm, making it ideal for large-scale clinical diagnostics of FCD.