TY - CHAP A1 - Mayer, Julius A1 - Baum, Daniel A1 - Ambellan, Felix A1 - von Tycowicz, Christoph T1 - A Soft-Correspondence Approach to Shape-based Disease Grading with Graph Convolutional Networks T2 - Proceedings of Machine Learning Research N2 - Shape analysis provides principled means for understanding anatomical structures from medical images. The underlying notions of shape spaces, however, come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of soft correspondences. In particular, we present a graph-based learning approach for morphometric classification of disease states that is based on a generalized notion of shape correspondences in terms of functional maps. We demonstrate the performance of the derived classifier on the open-access ADNI database for differentiating normal controls and subjects with Alzheimer’s disease. Notably, our experiment shows that our approach can improve over state-of-the-art from geometric deep learning. Y1 - 2022 VL - 194 SP - 85 EP - 95 ER -