A Generative Model Reveals the Influence of Patient Attributes on Fundus Images

Sarah Müller, Lisa M. Koch, Hendrik Lensch, Philipp Berens

Keywords: generative models, fundus images, latent space, patient attributes

Abstract: Screening for ophthalmic diseases routinely relies on retinal fundus images. These images are highly heterogeneous and little is known about how patient attributes such as age and ethnicity contribute to the variability in appearance. As the image variation due to such factors may ultimately confound automated image interpretation using deep learning models, understanding the influence of patient attributes on retinal fundus images is key for reliable AI applications in ophthalmology. Here, we draw on recent advances in generative modeling and present a population model of retinal fundus images which is capable of generating highly realistic images and allows for an analysis of how the patient attributes age and ethnicity are organized in the latent space of the generative model.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Application: Ophthalmology

Secondary Subject Area: Unsupervised Learning and Representation Learning

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