Search Results for author:
Found 5 papers, 1 papers with code
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
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.
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
We propose to take advantage of this property using bayesian deep learning, based on the assumption that epistemic uncertainties will correlate with anatomical deviations from a normal training set.
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data.
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
Generative Adversarial Network
Unsupervised Anomaly Detection
