A computer-implemented method (100) for extending a training
data set for
machine learning, the method comprising: - Providing a
data set of an object to be examined, wherein the
data set has coordinate values and measured values for each coordinate, wherein the data set is derived from an
image acquisition method of a computer tomograph, - Identifying an anomaly in a section of the data set that corresponds to a sub-area of the object to be examined, - Classifying the anomaly into a number of predefined classification classes using a first
machine learning model trained with a first training data set, - Determining a difference value of the anomaly compared to the trained first
machine learning model on the basis of a combination of novelty measures by means of a second
machine learning model trained with a second training data set, wherein the first training data set and the second training data set are created by a respective random selection of data sets from an overall training data set, and - Supplementing the first training data set with data concerning the identified anomaly if the difference value is above a first predefined threshold.