Unsupervised Change Detection using Joint Autoencoders for Age-Related Macular Degeneration Progression

Guillaume Dupont (1) , Ekaterina Kalinicheva (1) , Jérémie Sublime (1, 2) , Florence Rossant (1) , Michel Pâques (3)

1 ISEP - Institut Supérieur d'Electronique de Paris
2 LIPN - Laboratoire d'Informatique de Paris-Nord
3 CHNO - Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts

Age-Related Macular Degeneration (ARMD) is an eye disease that has been an important research field for two decades now. Researchers have been mostly interested in studying the evolution of le-sions that slowly causes patients to go blind. Many techniques ranging from manual annotation to mathematical models of the disease evolution bring interesting leads to explore. However, artificial intelligence for ARMD image analysis has become one of the main research focus to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced doctors. Within this context, in this paper, we propose a neural network architecture for change detection in eye fundus images to highlight the evolution of the disease. The proposed method is fully un-supervised, and is based on fully convolutional joint autoencoders. Our algorithm has been applied to several pairs of images from eye fundus images time series of ARMD patients, and has shown to be more effective than most state-of-the-art change detection methods, including non-neural network based algorithms that are usually used to follow the evolution of the disease.

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Guillaume Dupont, Ekaterina Kalinicheva, Jérémie Sublime, Florence Rossant, Michel Pâques. Unsupervised Change Detection using Joint Autoencoders for Age-Related Macular Degeneration Progression. Springer. ICANN 2020, Part II, 1, In press, LNCS 12397. ⟨hal-02963848⟩