Abstract #0658

dAUTOMAP: Decomposing AUTOMAP to Achieve Scalability and Enhance Performance

Jo Schlemper¹, Ilkay Oksuz², James Clough², Jinming Duan³, Andrew P. King², Julia A. Schnabel², Joseph V Hajnal², and Daniel Rueckert¹

¹Department of Computing, Imperial College London, London, United Kingdom, ²Biomedical Engineering, King's College London, London, United Kingdom, ³Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, United Kingdom

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present a novel way for decomposing the domain transformation, which makes the model scale linearly with the input size. We show the proposed method, termed dAUTOMAP, outperforms AUTOMAP with significantly fewer parameters.

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