@Radiology_AI

Interpretable, highly accurate segmentation models have the potential to provide substantial benefit for automated clinical workflows. Estimating the uncertainty in a model's prediction (predictive uncertainty) can help clinicians quantify, visualize, and communicate model performance. Variational inference, Monte Carlo dropout, and ensembles are reliable methods to estimate predictive uncertainty. Interpretable artificial intelligence is key for clinical translation of this technology. Artificial intelligence (AI) has seen a resurgence in popularity since the development of deep learning (DL), a method to learn representations within data with multiple levels of abstraction (1). DL frameworks have been widely successful for a variety of applications, including image object recognition and detection tasks where there is a particular interest in applying this technology to interpret complex medical images (2). As modern DL frameworks are structured through multiple hidden layers of network weights, these networks are coined as black box models.