# Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

arXiv: https://arxiv.org/abs/2502.16697 · HTML: https://mlsanity.com/paper/2502.16697/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 75 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 30 of 31)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI](https://mlsanity.com/paper/2003.07258/) (2020)
- [Evaluating Explainability for Graph Neural Networks](https://mlsanity.com/paper/2208.09339/) (2022)
- [Detecting Statistical Interactions from Neural Network Weights](https://mlsanity.com/paper/1705.04977/) (2017)
- [Model Interpretability through the Lens of Computational Complexity](https://mlsanity.com/paper/2010.12265/) (2020)
- [OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study](https://mlsanity.com/paper/2012.07261/) (2020)
- [Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry](https://mlsanity.com/paper/1811.11310/) (2018)
- [Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets](https://mlsanity.com/paper/2102.03444/) (2021)
- [Supervised machine learning based multi-task artificial intelligence classification of retinopathies](https://mlsanity.com/paper/1905.04224/) (2019)
- [DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images](https://mlsanity.com/paper/2304.02389/) (2023)

## Built on by (most cited first, 4 of 4)

- [Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis](https://mlsanity.com/paper/2503.09808/) (2025)
- [A Graph-Based Framework for Interpretable Whole Slide Image Analysis](https://mlsanity.com/paper/2503.11846/) (2025)
- [Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning](https://mlsanity.com/paper/2512.11060/) (2025)
- [Representation learning from OCT images](https://mlsanity.com/paper/2605.02589/) (2026)

## Abstract (arXiv)

Interpretability is crucial for utilizing machine learning models as clinical decision support tools 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 to guide diagnosis, although biomarker-based classification often suffers from drastic information loss compared to raw medical images. This work proposes a method that preserves the rich imaging information while simultaneously enhancing the interpretability of predictions for diabetic retinopathy staging from optical coherence tomography angiography (OCTA) images. The core contribution of our method is 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 established, efficient graph neural network architecture. We compare our method against established methods, including classical biomarker-based classifiers, convolutional neural networks (CNNs), and vision transformers in predicting the clinically assigned DR stage based on color fundus photography images. We find stage agreement rates of our method and alternative vision model based classifiers saturating at AUC-ROC values of 84%. Crucially, we use our biology-informed graph to provide explanations of great detail. Our approach surpasses existing methods in precisely localizing and identifying abnormal vessels and non-perfusion areas. Our approach sets the stage for the interpretable identification of patients who require special attention due to their traceable microvascular changes, only observable using the details of OCTA images.