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Found 6 papers, 1 papers with code
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects.
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
Identifying and Categorizing Anomalies in Retinal Imaging Data
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice.
