Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Journal:
arXiv
Published Date:
Feb 23, 2025
Abstract
Interpretability is crucial to enhance trust in machine learning models for
medical diagnostics. However, most state-of-the-art image classifiers based on
neural networks are not interpretable. As a result, clinicians often resort to
known biomarkers for diagnosis, although biomarker-based classification
typically performs worse than large neural networks. This work proposes a
method that surpasses the performance of established machine learning models
while simultaneously improving prediction interpretability for diabetic
retinopathy staging from optical coherence tomography angiography (OCTA)
images. Our method is based on a novel biology-informed heterogeneous graph
representation that models retinal vessel segments, intercapillary areas, and
the foveal avascular zone (FAZ) in a human-interpretable way. This graph
representation allows us to frame diabetic retinopathy staging as a graph-level
classification task, which we solve using an efficient graph neural network. We
benchmark our method against well-established baselines, including classical
biomarker-based classifiers, convolutional neural networks (CNNs), and vision
transformers. Our model outperforms all baselines on two datasets. Crucially,
we use our biology-informed graph to provide explanations of unprecedented
detail. Our approach surpasses existing methods in precisely localizing and
identifying critical vessels or intercapillary areas. In addition, we give
informative and human-interpretable attributions to critical characteristics.
Our work contributes to the development of clinical decision-support tools in
ophthalmology.
