Abstract: The present disclosure relates to analysis techniques to determine at run time whether a machine-learning model is applicable to a new set of input data. Particularly, aspects are directed to inputting an input query into a machine-learning model, generating, using the machine-learning model comprising model parameters learned for a particular task, a prediction associated with the task based on the input query, obtaining one or more metrics for generalization of the machine-learning model on the input query, the one or more metrics being computed using black-box and/or clear-box techniques for predicting a correctness of a model on a sample-by-sample basis (additionally applicable on a population level), by analyzing how the machine-learning model responds to the input query, and outputting a prediction of model generalization for the machine-learning model based on the one or more metrics.

Abstract: The present disclosure relates to techniques for non-invasive tumor identification, classification, and grading using mixed exam-, region-, and voxel-wise supervision.

Abstract: The present disclosure relates to techniques for fusing deep learning-based image reconstructions with noisy image measurements with provable assurances that the resulting improved image does not remove the information content of the original noisy measurements or image. Particularly, aspects are directed to obtaining measurement data from an imaging modality, generating a base image by solving an optimization problem using at least a signal model and the measurement data, generating, using a deep-learning model, a predicted image based on the measurement data, selecting a modified operator based on the signal model, generating an enhanced image by solving the modified optimization problem using at least: (i) the base image or the measurement data, (ii) the signal model, (iii) the predicted image, and (iv) the modified operator, and outputting the enhanced image.

Abstract: The present disclosure relates to techniques for non-invasive tumor identification, classification, and grading using mixed exam-, region-, and voxel-wise supervision.