Core Concepts
Retinal fundus image disentanglement enables controllable and realistic image generation by effectively separating patient attributes from technical factors.
Abstract
網膜眼底圖像在早期檢測眼部疾病方面起著關鍵作用。然而,技術因素對這些圖像的影響可能對眼科學中可靠的AI應用造成挑戰。本文介紹了一種新的人口模型,有效地將患者屬性與相機效果分離,從而實現可控且高度逼真的圖像生成。通過定性和定量分析,展示了這種新型損失函數在分離所學子空間方面的有效性。結果表明,該模型提供了一個新的視角,揭示了網膜眼底圖像中患者屬性和技術混淆因素之間複雜關係。
Stats
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.
Quotes
"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."