Algorithmic statistics of retinal images
There has been a tremendous amount of image processing and machine learning research to measure and classify disease progression from live optical coherence tomography (OCT) imaging of the retina. The images considered here are large, complex, three-dimensional (3-D) and difficult to visualize effectively. Many current supervised machine learning approaches, \emph{e.g.} neural networks, are non-metric meaning that any features or measurements generated can introduce systematic distortion that may be correlated with underlying non-meaningful physiological differences. Here we present a metric learning approach using the normalized compression distance (NCD) combined with anisotropic struct...
| Authors | Loan Huynh, Ronald Zambrano, Layton Aho et al. |
|---|---|
| Primary category | eess.IV |
| Published | 6 August 2026 |
| Last updated at source | 12 August 2026 |
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