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A Soft-Correspondence Approach to Shape-based Disease Grading with Graph Convolutional Networks

  • 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.

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Author:Julius Mayer, Daniel BaumORCiD, Felix AmbellanORCiD, Christoph von Tycowicz
Document Type:In Proceedings
Parent Title (English):Proceedings of Machine Learning Research
First Page:85
Last Page:95
Fulltext Url:https://openreview.net/pdf?id=Wa6mfpfr29o
Year of first publication:2022
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