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

Unsupervised Image Segmentation using Mutual Mean-Teaching

no code implementations • 16 Dec 2020 • Zhichao Wu, Lei Guo, Hao Zhang, Dan Xu

Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision.

Image Segmentation Segmentation +2

Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning

no code implementations • 21 Oct 2019 • Antoine Rivail, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu, Hrvoje Bogunović

Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression.

Self-Supervised Learning