Project
EF1-18
Manifold-Valued Graph Neural Networks
Project Heads
Christoph von Tycowicz, Gabriele Steidl
Project Members
Martin Hanik
Project Duration
01.01.2022 − 31.12.2023
Located at
FU Berlin
Description
Geometry-aware, data-analytic approaches improve understanding and assessment of pathophysiological processes. We will derive a new theoretical framework for deep neural networks that can cope with geometric data and apply it for classification of musculoskeletal illness from both shape and movement patterns.
External Website
Related Publications
- Cristoph von Tycowicz. Towards shape-based knee osteoarthritis classification using graph convolutional networks, In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020), pp. 750–753, 2020. Preprint.
- Ivan Sipiran et al., SHREC 2021: Retrieval of Cultural Heritage Objects, Computers and Graphics, Vol. 100, pp. 1–20, 2021.
- Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui, Islem Rekik. Predicting cognitive scores with graph neural networks through sample selection learning, Brain Imaging and Behavior, Vol.16, pp. 1123-1138, 2022. Preprint.
- Ariel Caputo et al. SHREC 2022 Track on Online Detection of Heterogeneous Gestures, Computers and Graphics, Vol.107, pp. 241-251, 2022. Preprint.
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