A system for processing a trained model configured for image classification or image segmentation, comprising: - a data interface for accessing: - model data representing a trained model trained for image classification or image segmentation, wherein the trained model is a trained neural network, and - training data acquired from a medical scan apparatus on which the trained model is trained, wherein the training data comprises multiple images; - a processor subsystem configured to: - characterize the training data by: - applying the trained model to the training data to obtain intermediate output of the trained model, wherein the intermediate output comprises activation values of a subset of hidden units of the trained neural network, and - determining a numerical characteristic which is descriptive of the training data based on the intermediate output of the trained model; - encode the numerical characteristic as metadata; and - associate the metadata with the model data to enable an entity applying the trained model to input data acquired in a similar manner as the training data and thereby obtaining further intermediate output of the trained model to determine whether the input data conforms to the numerical characteristic of the training data of the trained model based on the further intermediate output. - a data interface for accessing: - model data representing a trained model trained for image classification or image segmentation, wherein the trained model is a trained neural network, and - training data acquired from a medical scan apparatus on which the trained model is trained, wherein the training data comprises multiple images; - model data representing a trained model trained for image classification or image segmentation, wherein the trained model is a trained neural network, and - training data acquired from a medical scan apparatus on which the trained model is trained, wherein the training data comprises multiple images; - a processor subsystem configured to: - characterize the training data by: - applying the trained model to the training data to obtain intermediate output of the trained model, wherein the intermediate output comprises activation values of a subset of hidden units of the trained neural network, and - determining a numerical characteristic which is descriptive of the training data based on the intermediate output of the trained model; - encode the numerical characteristic as metadata; and - associate the metadata with the model data to enable an entity applying the trained model to input data acquired in a similar manner as the training data and thereby obtaining further intermediate output of the trained model to determine whether the input data conforms to the numerical characteristic of the training data of the trained model based on the further intermediate output. - characterize the training data by: - applying the trained model to the training data to obtain intermediate output of the trained model, wherein the intermediate output comprises activation values of a subset of hidden units of the trained neural network, and - determining a numerical characteristic which is descriptive of the training data based on the intermediate output of the trained model; - applying the trained model to the training data to obtain intermediate output of the trained model, wherein the intermediate output comprises activation values of a subset of hidden units of the trained neural network, and - determining a numerical characteristic which is descriptive of the training data based on the intermediate output of the trained model; - encode the numerical characteristic as metadata; and - associate the metadata with the model data to enable an entity applying the trained model to input data acquired in a similar manner as the training data and thereby obtaining further intermediate output of the trained model to determine whether the input data conforms to the numerical characteristic of the training data of the trained model based on the further intermediate output.