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In this contribution we present a method for solving the inverse problem in electric impedance tomography with neural networks. The problem of reconstructing the conductivity distribution inside an object from potential measurements on the surface is known to be ill-posed, requiring efficient regularization techniques. We demonstrate that a statistical inverse solution, where the mean of the inverse mapping is approximated with a neural network gives promising results. We study the effect ofdoi:10.1109/ijcnn.1999.830787 dblp:conf/ijcnn/LampinenVL99 fatcat:dwv44wd4pbeqxlypwx7hukmdde