Disentangling representations of retinal images with generative models
Statistikk
Retinal fundus images play a crucial role in early detection of eye diseases.
Deep learning approaches have shown potential for detecting cardiovascular risk factors and neurological disorders.
Technical factors like camera type, image quality, and illumination levels can affect image generation.
Subspace learning combines representation learning and disentanglement to address confounding factors.
Generative models like VAEs offer valuable inductive bias for representation learning.
Sitater
"Fundus images can be used to detect not only eye diseases but also cardiovascular risk factors or neurological disorders using deep learning."
"Our model provides a new perspective on the complex relationship between patient attributes and technical confounders in retinal fundus image generation."