A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography

no code implementations • 5 Apr 2022 • Roy Schwartz, Hagar Khalid, Sandra Liakopoulos, Yanling Ouyang, Coen de Vente, Cristina González-Gonzalo, Aaron Y. Lee, Robyn Guymer, Emily Y. Chew, Catherine Egan, Zhichao Wu, Himeesh Kumar, Joseph Farrington, Clara I. Sánchez, Adnan Tufail

Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen.

Classification Image Segmentation +4