Explainable unsupervised multi-modal image registration using deep
  networks
Chengjia Wang Giorgos Papanastasiou
Clinical decision making from magnetic resonance imaging (MRI) combines
complementary information from multiple MRI sequences (defined as
'modalities'). MRI image registration aims to geometrically 'pair' diagnoses
from different modalities, time points and slices. Both intra- and
inter-modality MRI registration are essential components in clinical MRI
settings. Further, an MRI image processing pipeline that can address both afine
and non-rigid registration is critical, as both types of deformations may be
occuring in real MRI data scenarios. Unlike image classification,
explainability is not commonly addressed in image registration deep learning
(DL) methods, as it is challenging to interpet model-data behaviours against
transformation fields. To properly address this, we incorporate Grad-CAM-based
explainability frameworks in each major component of our unsupervised
multi-modal and multi-organ image registration DL methodology. We previously
demonstrated that we were able to reach superior performance (against the
current standard Syn method). In this work, we show that our DL model becomes
fully explainable, setting the framework to generalise our approach on further
medical imaging data.