The goal of this project is to automate the screening workflow of eye diseases using innovative techniques based on artificial neural networks. The scientific challenge of this project is to develop dynamic learning strategies that allow deep learning systems to continuously learn new concepts and adapt to new data over time, in order to increase their robustness in changing clinical settings and maintain their accuracy through their lifetime. This will be done by a) allowing deep learning systems to provide interpretable predictions and communicate with human experts; b) increasing specialization of deep learning systems over time, and c) dynamically modifying deep learning parameters and architecture based on human experts’ feedback. The project is part of the TTW Perspectief DLMedia project and partially funded by Thirona B.V.
Visual interpretability of automated screening in a color fundus image predicted as referable diabetic retinopathy.