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Invited Talk: Adrian Dalca - Unsupervised Learning of Image Correspondences in Medical Image Analysis
Image registration is fundamental to many tasks in image analysis. Classical image 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 introduce various models that leverage learning paradigms to enable deformable medical image registration more accurately and substantially faster than traditional methods, crucially enabling new research directions and applications. Based on these models I will discuss a learning framework for building deformable templates, which play a fundamental role in these analyses. This learning approach to template construction can yield a new class of on-demand conditional templates, enabling new analysis. I will also present recent or ongoing models, such as modality-invariant learning-based registration methods that work on unseen test-time contrasts, and hyperparameter-agnostic learning for image registration that removes the need to train different models for different hyperparameters.

Adrian V. Dalca is Assistant Professor at Harvard Medical School, and research scientist at the Massachusetts Institute of Technology. He obtained his PhD from CSAIL, MIT, and his research focuses on probabilistic models and machine learning techniques to capture relationships between medical images, clinical diagnoses, and other complex medical data. His work spans medical image analysis, computer vision, machine learning and computational biology. He received his BS and MS in Computer Science from the University of Toronto.
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