MLCN 2021 Keynote 1: Dr. Adrian Dalca

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.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s