  Retinal fundus images play a crucial role in the early detection of eye
diseases and, using deep learning approaches, recent studies have even
demonstrated their potential for detecting cardiovascular risk factors and
neurological disorders. However, the impact of technical factors on these
images can pose challenges for reliable AI applications in ophthalmology. For
example, large fundus cohorts are often confounded by factors like camera type,
image quality or illumination level, bearing the risk of learning shortcuts
rather than the causal relationships behind the image generation process. Here,
we introduce a novel population model for retinal fundus images that
effectively disentangles patient attributes from camera effects, thus enabling
controllable and highly realistic image generation. To achieve this, we propose
a novel disentanglement loss based on distance correlation. Through qualitative
and quantitative analyses, we demonstrate the effectiveness of this novel loss
function in disentangling the learned subspaces. Our results show that our
model provides a new perspective on the complex relationship between patient
attributes and technical confounders in retinal fundus image generation.
