Abstract: Systems, apparatus, articles of manufacture, and methods to detect anomalies in three-dimensional (3D) images are disclosed. Example apparatus disclosed herein generate a first two-dimensional (2D) anomaly map corresponding to a first 2D image slice of a 3D image, the first 2D image slice corresponding to a first axis of the 3D image. Disclosed example apparatus also generate a second 2D anomaly map corresponding to a second 2D image slice of the 3D image, the second 2D image slice corresponding to a second axis of the 3D image. Disclosed example apparatus further generate a 3D anomaly volume based on the first 2D anomaly map and the second 2D anomaly detection, the 3D anomaly volume corresponding to the 3D image.

Abstract: An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to extract neural network model features from deployment data, identify out-of-distribution data based on the neural network model features, identify samples with the out-of-distribution data to generate one or more scores associated with post-deployment data drift, and classify post-deployment data based on the one or more scores.