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Found 5 papers, 1 papers with code
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation
It then exploits a novel multi-modal contrastive learning framework COEP to integrate other retinal imaging modalities, such as fundus autofluorescence and infrared retinal imaging, into OCTCube, efficiently extending it into multi-modal foundation models.
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.
Forecasting Future Humphrey Visual Fields Using Deep Learning
Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database.
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.
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR.
Ranked #13 on
Retinal OCT Disease Classification
on Srinivasan2014
General Classification
Retinal OCT Disease Classification
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