Pelvis conductivity is typically reconstructed with Helmholtz-based EPT. To overcome typical limitations of Helmholtz-based EPT in this challenging body site we explored reconstructing pelvis conductivity with deep learning. A 3D patch-based convolutional neural network was trained on in silica MR data (either a full complex B1+ field or transceive phase only) with realistic noise levels. These data were related to realistic pelvic anatomies and electrical properties. Preliminary results indicate that the network retrieved anatomically-detailed conductivity maps, without a priori anatomical knowledge given in input. Quantitatively, conductivity estimates on in vivo volunteer MR data were in line with literature.