  We explore different curriculum learning methods for training convolutional
neural networks on the task of deformable pairwise 3D medical image
registration. To the best of our knowledge, we are the first to attempt to
improve performance by training medical image registration models using
curriculum learning, starting from an easy training setup in the first training
stages, and gradually increasing the complexity of the setup. On the one hand,
we consider two existing curriculum learning approaches, namely curriculum
dropout and curriculum by smoothing. On the other hand, we propose a novel and
simple strategy to achieve curriculum, namely to use purposely blurred images
at the beginning, then gradually transit to sharper images in the later
training stages. Our experiments with an underlying state-of-the-art deep
learning model show that curriculum learning can lead to superior results
compared to conventional training.
