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Exploring Graphs as Data Representation for Disease Classification in Ophthalmology

MCML Authors

Link to Profile Martin Menten

Martin Menten

Dr.

JRG Leader AI for Vision

Artificial Intelligence in Healthcare and Medicine

Abstract

Interpretability, particularly in terms of human understandable concepts, is essential for building trust in machine learning models for disease classification. However, state-of-the-art image classifiers exhibit limited interpretability, posing a significant barrier to their acceptance in clinical practice. To address this, our work introduces two graph representations of the retinal vasculature, aiming to bridge the gap between high-performance classifiers and human-understandable interpretability concepts in ophthalmology. We use these graphs with the aim of training graph neural networks (GNNs) for disease staging. First, we formally and experimentally show that GNNs can learn known clinical biomarkers. In that, we show that GNNs can learn human interpretable concepts. Next, we train GNNs for disease staging and study how different aggregation strategies lead the GNN to learn more and less human interpretable features. Finally, we propose a visualization for integrated gradients on graphs, which allows us to identify if GNN models have learned human-understandable representations of the data.

inproceedings

GRAIL @MICCAI 2024

6th Workshop on GRaphs in biomedicAl Image anaLysis at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. Marrakesh, Morocco, Oct 06-10, 2024.

Authors

L. Lux • A. H. Berger • M. Romeo-Tricas • M. J. Menten • D. Rückert • J. C. Paetzold

Links

DOI URL

Research Area

 C1 | Medicine

BibTeXKey: LBR+24

#p_menten #p_rueckert
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