  We explore three representative lines of research and demonstrate the utility
of our methods on a classification benchmark of brain cancer MRI data. First,
we present a capsule network that explicitly learns a representation robust to
rotation and affine transformation. This model requires less training data and
outperforms both the original convolutional baseline and a previous capsule
network implementation. Second, we leverage the latest domain adaptation
techniques to achieve a new state-of-the-art accuracy. Our experiments show
that non-medical images can be used to improve model performance. Finally, we
design a spiking neural network trained on the Intel Loihi neuromorphic chip
(Fig. 1 shows an inference snapshot). This model consumes much lower power
while achieving reasonable accuracy given model reduction. We posit that more
research in this direction combining hardware and learning advancements will
power future medical imaging (on-device AI, few-shot prediction, adaptive
scanning).
