Koninklijke Philips Corporation N.V.

Nederlandse Organisatie Voor Toegepast-natuurwetenschappelijk Onderzoek Tno

Koninklijke Kpn N.v.

Vlado Menkovski

Erik Bresch

Bryan Conroy

Dimitrios Mavroeidis

Teun Van Den Heuvel

Caroline Denise Francoise Raynaud

Frank Den Hartog

Asif Rahman

Antonio Liotta

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.

Using a neural network

A system and method are provided for enabling atlas registration in medical imaging, said atlas registration comprising matching a medical atlasto a medical image. 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.

Medical atlas registration

Growing community of inventors

Eindhoven, Netherlands

Vlado Menkovski