47 citations · 62 across the 10 of their papers we have counts for
5 papers · 1 filter
Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
Stephen Odaibo
In Bayesian machine learning, the posterior distribution is typically computationally intractable, hence variational inference is often required. In this approach, an evidence lowe…
retina-VAE: Variationally Decoding the Spectrum of Macular Disease
Stephen G. Odaibo
In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-base…
Is 'Unsupervised Learning' a Misconceived Term?
Stephen G. Odaibo
Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into ; whe…
Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina
Stephen G. Odaibo, M. D., M. S.
We report, to our knowledge, the first end-to-end application of Generative Adversarial Networks (GANs) towards the synthesis of Optical Coherence Tomography (OCT) images of the re…
Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study
Stephen G. Odaibo, Mikelson MomPremier, Richard Y. Hwang +3
Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud…