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SHR Neuro Cancer Cardio Lipid Metab Microb

Saranti, A; Pfeifer, B; Gollob, C; Stampfer, K; Holzinger, A.
From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges
WIRES DATA MIN KNOWL. 2024; Doi: 10.1002/widm.1554
Web of Science FullText FullText_MUG

Leading authors Med Uni Graz: Holzinger Andreas; Saranti Anna
Co-authors Med Uni Graz: Pfeifer Bastian
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Abstract:: We present an exciting journey from 3D point-cloud data (PCD) to the state of the art in graph neural networks (GNNs) and their evolution with explainable artificial intelligence (XAI), and 3D geometric priors with the human-in-the-loop. We follow a simple definition of a "digital twin," as a high-precision, three-dimensional digital representation of a physical object or environment, captured, for example, by Light Detection and Ranging (LiDAR) technology. After a digression into transforming PCD into images, graphs, combinatorial complexes and hypergraphs, we explore recent developments in geometric deep learning (GDL) and provide insight into the application of these network architectures for analyzing and learning from graph-structured data. We emphasize the importance of the explainability of these models and recognize that the ability to interpret and validate the results of complex models is a crucial aspect of their wider adoption. This article is categorized under: Technologies > Artificial Intelligence
Find related publications in this database (Keywords): digital twin; explainable AI; geometric deep learning; graph neural networks; graphs; LiDAR; point-cloud data; topologic neural networks