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Mueller, T.T.* ; Starck, S.* ; Feiner, L.F.* ; Bintsi, K.M.* ; Rueckert, D.* ; Kaissis, G.

Extended Graph Assessment Metrics for Regression and Weighted Graphs.

In:. Berlin [u.a.]: Springer, 2024. 14-26 (Lect. Notes Comput. Sc. ; 14373 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
When re-structuring patient cohorts into so-called population graphs, initially independent patients can be incorporated into one interconnected graph structure. This population graph can then be used for medical downstream tasks using graph neural networks (GNNs). The construction of a suitable graph structure is a challenging step in the learning pipeline that can have a severe impact on model performance. To this end, different graph assessment metrics have been introduced to evaluate graph structures. However, these metrics are limited to classification tasks and discrete adjacency matrices, only covering a small subset of real-world applications. In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and weighted graphs. We focus on two GAMs in particular: homophily and cross-class neighbourhood similarity (CCNS). We extend the notion of GAMs to more than one hop, define homophily for regression tasks, as well as continuous adjacency matrices, and propose a lightweight CCNS distance for discrete and continuous adjacency matrices. We show the correlation of these metrics with model performance on different medical population graphs and under different learning settings, using the TADPOLE and UKBB datasets1(The source code can be found at https://github.com/tamaramueller/ExtendedGAMs).
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Graph Assessment Metrics ; Graph Neural Networks ; Medical Population Graphs
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14373 LNCS, Heft: , Seiten: 14-26 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen NextGenerationEU of the European Union
DOD ADNI (Department of Defense)
Alzheimers Disease Neuroimaging Initiative (ADNI) (National Institutes of Health)
BMBF
ERC