System and method for the quality assurance of data-based models

US12430538B2 · kind B2 · utility

Assignee

• AICURA MEDICAL GMBH · DE

Inventors

• Sebastian NIEHAUS · Buxtehude, DE
• Michael Diebold · Berlin, DE
• Janis REINELT · Leipzig, DE
• Daniel LICHTERFELD · Berlin, DE

Key dates

• Filing date: Dec 23, 2020
• Grant date: Sep 30, 2025
• Expiry date: Jan 12, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/094
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The invention relates to a system which, on the one hand, has a classifier that is formed by a discriminative neural network and that implements a binary class model or a multi-class model. On the other hand, the system has a model-based sample generator that is formed by a generative neural network. Both the classifier and the model-based sample generator are trained—for a corresponding class—with the same training data records and therefore embody models that correspond to one another for this class.The invention also relates to a method for determining a quality criterion for input data records for a classifier with a discriminative neural network. The classifier has been trained with training data records and represents a classification model for a class.According to the method, a model-based sample generator with a generative neural network is initially provided and trained with the same training data records that were used to train the classifier.Subsequently, by means of the trained model-based sample generator and an input data record based on random values, an artificial data record is generated, which is representative of the classification model embodied by the classifie…