Title: Unsupervised Learning of Image Correspondences in Neuroimaging
Abstract: Image alignment, or registration, is fundamental to many neuroimaging tasks. Classical neuroimaging registration methods have undergone decades of technical development, but are often prohibitively slow since they solve an optimization problem for each 3D image pair. In this talk, I will first introduce the modern deep learning paradigm that enables deformable medical image registration more accurately and substantially faster than traditional methods. Based on these models I will discuss new learning frameworks now possible for a variety of tasks, such as building a new class of on-demand conditional templates to enable new neuroimaging applications. I will discuss other recent exciting directions, such as modality-invariant learning-based registration methods that work on unseen test-time contrasts, and hyperparameter-agnostic learning for neuroimage registration.