Exploring Deep Registration Latent Spaces

@inproceedings{Estienne2021ExploringDR,
  title={Exploring Deep Registration Latent Spaces},
  author={Th{\'e}o Estienne and Maria Vakalopoulou and Stergios Christodoulidis and Enzo Battistella and Th'eophraste Henry and Marvin Lerousseau and Amaury Leroy and Guillaume Chassagnon and Marie-Pierre Revel and Nikos Paragios and Eric Deutsch},
  booktitle={DART/FAIR@MICCAI},
  year={2021}
}
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretability of deep learning-based registration methods. In particular, with the appropriate model architecture and using a simple linear projection, we decompose the encoding space, generating a new basis, and we empirically show that this basis captures various decomposed anatomically aware geometrical transformations. We… Expand

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