CVAE-based Causal Representation Learning from Retinal Fundus Images for Age Related Macular Degeneration(AMD) Prediction

Regarding relatively poor prognosis and acute vision impairment, analyzing Age-Related Macular Degeneration, or AMD has been one of the most important tasks in retinal disease analysis. Especially, constructing methods to analyze and predict Wet AMD, which is characterized by rapid RPE damage due to neovascularization, has been a demanding task for many ophthalmologists for decades. Recently, with advancements in ML/DL frameworks and computer vision AI, these previous efforts are now leading to drastic enhancements in AMD prediction and mechanism analysis. Specifically, use of attention mechanism based CNNs or XAI methods are leading to higher performance in predicting AMD status and reliable explanations. Under current success in the usage of cutting-edge techniques, this research implemented a novel latent causal representation learning framework to further enhance AI-based models to comprehend complex causal AMD mechanisms with only access to retinal fundus images, while constructing a more reliable type of AMD prediction model. Results show that valid convolutional VAE and GAE based explicit latent causal modeling can lead to successful causal disentanglements of underlying AMD mechanisms, while returning essential causal factors that can be utilized to reliably distinguish normal fundus and AMD fundus images in downstream tasks such as diagnosis prediction.