  Ocular diseases, ranging from diabetic retinopathy to glaucoma, present a
significant public health challenge due to their prevalence and potential for
causing vision impairment. Early and accurate diagnosis is crucial for
effective treatment and management.In recent years, deep learning models have
emerged as powerful tools for analysing medical images, including ocular
imaging . However, challenges persist in model interpretability and uncertainty
estimation, which are critical for clinical decision-making. This study
introduces a novel application of GFlowOut, leveraging the probabilistic
framework of Generative Flow Networks (GFlowNets) to learn the posterior
distribution over dropout masks, for the classification and analysis of ocular
diseases using eye fundus images. We develop a robust and generalizable method
that utilizes GFlowOut integrated with ResNet18 and ViT models as backbone in
identifying various ocular conditions. This study employs a unique set of
dropout masks - none, random, bottomup, and topdown - to enhance model
performance in analyzing ocular images. Our results demonstrate that the
bottomup GFlowOut mask significantly improves accuracy, outperforming the
traditional dropout approach.
