Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.

Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.

Abstract: A system and method are provided for enabling atlas registration in medical imaging, said atlas registration comprising matching a medical atlas 300, 302 to a medical image 320. The system and method may execute a Reinforcement Learning (RL) algorithm to learn a model for matching the medical atlas to the medical image, wherein said learning is on the basis of a reward function quantifying a degree of match between the medical atlas and the medical image. The state space of the RL algorithm may be determined on the basis of a set of features extracted from i) the atlas data and ii) the image data. As such, a model is obtained for medical atlas registration without the use, or with a reduced use, of heuristics. By using a machine learning based approach, the solution can easily be applied to different atlas matching problems, e.g., to different types of medical atlases and/or medical images.

Abstract: A system and method are provided for enabling atlas registration in medical imaging, said atlas registration comprising matching a medical atlas 300, 302 to a medical image 320. The system and method may execute a Reinforcement Learning (RL) algorithm to learn a model for matching the medical atlas to the medical image, wherein said learning is on the basis of a reward function quantifying a degree of match between the medical atlas and the medical image. The state space of the RL algorithm may be determined on the basis of a set of features extracted from i) the atlas data and ii) the image data. As such, a model is obtained for medical atlas registration without the use, or with a reduced use, of heuristics. By using a machine learning based approach, the solution can easily be applied to different atlas matching problems, e.g., to different types of medical atlases and/or medical images.

Abstract: A method is described for assessing the resources of a network by performing a first measurement to measure resources in the network, constructing a model of network activity from an assessment of activity in the network, and deriving an estimate of confidence for the validity of the first measurement from the model of network activity. The first measurement may be reported along with the estimate of confidence or the first measurement may be reported if the estimate of confidence is within a limit, otherwise a second measurement is performed and reported. The method improves delivery of services to networks by allowing a measurement of resources to be coupled to an estimate of validity of that the measurement. A system and a gateway device for assessing the resources in a network is also described.

Abstract: The present invention relates to a method for calculating user experience perception of the quality of monitored integrated telecommunications operator services. For this purpose, data from the monitoring of user services is used, along with questionnaires previously completed by a representative sample of users for subsequent combination by means of correlation algorithms, and after they have been put through automatic learning algorithms, obtaining a value for the quality of the experience, which implies an estimate of the quality of service perceived by the user of said service. Lastly, the network parameters that most affect the QoE as a function of the relevance thereof for predictions of quality are automatically identified in order to provide the values needed to attain a certain quality of experience as defined by the user.