A vision-GNN framework for retinopathy classification using optical coherence tomography

Mingzhe Hu; Jing Wang; Jacob Wynne; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2653615

7 April 2023 A vision-GNN framework for retinopathy classification using optical coherence tomography

Mingzhe Hu, Jing Wang, Jacob Wynne, Tian Liu, Xiaofeng Yang

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650Z (2023) https://doi.org/10.1117/12.2653615
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Retinopathy refers to pathologies of the retina that can ultimately result in vision impairment and blindness. Optical Coherence Tomography (OCT) is a technique to image these diseases, aiding in the early detection of retinal damage, which may mitigate the risk of vision loss. In this work, we propose an end-to-end Graph Neural Network (GNN) pipeline that can extract deep graph-based features for multi-class retinopathy classification for the first time. To our knowledge, this is also the first work applying Vision-GNN for OCT image analysis. We trained and tested the proposed GNN on a public OCT retina dataset divided into four categories (Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen). Using our method, we achieve an average accuracy of 99.07% over four classes proving the effectiveness of a deep learning classifier for OCT images with graph-based features. This work lays the foundation to apply GNNs for OCT imaging to aid the early detection of retinal damage.

Conference Presentation

Citation Download Citation

Mingzhe Hu, Jing Wang, Jacob Wynne, Tian Liu, and Xiaofeng Yang "A vision-GNN framework for retinopathy classification using optical coherence tomography", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650Z (7 April 2023); https://doi.org/10.1117/12.2653615

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