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We present a multi-input encoder-decoder neural network model able to perform MR image synthesis from any subset of its inputs, outperforming prior methods in both single and multi-input settings. This is achieved by encouraging the network to learn a modality invariant latent embedding during training. We demonstrate that a spatial transformer module  can be included in our model to automatically correct misalignment in the input data. Thus, our model is robust both to missing and misaligned data at test time. Finally, we show that the model’s modular nature allows transfer learning to different datasets.
|Title of host publication||Medical Image Computing and Computer-Assisted Intervention-MICCAI 2017|
|Subtitle of host publication||20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III|
|Number of pages||9|
|Publication status||Published - 4 Sep 2017|
|Event||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada|
Duration: 11 Sep 2017 → 13 Sep 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017|
|Period||11/09/17 → 13/09/17|
- Neural network
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