In the past years, deep learning methods have taken the medical imaging community by storm. Convolutional neural networks (CNNs) excel at 2D or 3D image analysis, but at the same time, there is a growing realization that not all data is organized on an ordered grid. In many real-world medical applications, for example, genetics and brain imaging, the available data is naturally represented in a non-Euclidean space (e.g., graphs and manifolds). Moreover, data with which the medical imaging community is working contains many translational, rotational, and other symmetries that can be exploited by incorporation in problem design or neural network architectures. For this reason, geometric deep learning has gained significant popularity in the medical domain. This has been reflected in a significant increase in the body of literature using geometric deep learning for challenging medical imaging tasks including image registration, segmentation, and classification.

In this workshop, we aim to draw attention to current developments in geometric deep learning for medical image analysis. The main objective of our workshop is to expose the vast richness of geometric structure to be found in medical image data and show how to leverage it in neural network design. We will provide a discussion on the latest state-of-the-art by having invited expert speakers on the topic. We also aim to cover current challenges and opportunities in the area. Our objective is to inspire researchers through a day of exciting keynotes and contributed talks, showing how to design and/or apply methods that leverage geometric structure in imaging problems, e.g., through group convolutions, mesh CNNs, or graph neural networks with geometric priors. The objectives of the Geometric deep learning in medical image analysis (GeoMedIA) workshop are to (a) bring together experts on geometric deep learning in medical image analysis to push the state of the art; (b) hear from invited speakers, and (c) to identify challenges and opportunities for further research.

Contributed papers will be assessed by an expert program committee, and we will provide a best paper award. We will solicit both full papers and short papers. Full papers must contain novel work. They will be published in the workshop proceedings and may be considered for oral presentation. Short papers may contain already published work or work that is under review elsewhere. They will only be considered for poster presentation and will not be included in the workshop proceedings.

GeoMedIA is a MICCAI-endorsed event. GeoMedIA is proudly sponsored by ELLIS, the European Laboratory for Learning and Intelligent Systems. The workshop is further funded by the Dutch Research Council (NWO) through the research programme VENI with project 17290: Context-Aware Artificial Intelligence in Medical Image Analysis.