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Quantitative Characterization of Retinal Features in Translated OCTA
Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware.
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models
CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.
Quantitative optical coherence tomography reveals rod photoreceptor degeneration in early diabetic retinopathy
Methods: OCT images were acquired from normal eyes, diabetic eyes with no diabetic retinopathy (NoDR) and mild DR. Quantitative features, including length features quantifying band distances and reflectance intensity features among the external limiting membrane (ELM), inner segment ellipsoid (ISe), interdigitation zone (IZ) and retinal pigment epithelium (RPE) were determined.
