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Found 3 papers, 0 papers with code
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
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.
Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs
Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in the target domain.
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels.
