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retina-VAE: Variationally Decoding the Spectrum of Macular Disease
In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease.
Is 'Unsupervised Learning' a Misconceived Term?
In particular, we argue that clustering algorithms such as k-means, and dimensionality reduction algorithms such as principal component analysis, variational autoencoders, and deep belief networks are each internally supervised by the data itself to learn their respective representations of its features.
Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina
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 retina.
Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study
And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans.
A Sinc Wavelet Describes the Receptive Fields of Neurons in the Motion Cortex
Therefore in modeling receptive fields, it is essential to ensure that the response properties of neurons in a given level can be generated by combining the response models of neurons in its input levels.
The Gabor-Einstein Wavelet: A Model for the Receptive Fields of V1 to MT Neurons
Our analysis and computer simulations show that the distribution of temporal frequency filtering properties along the motion processing stream is a direct effect of the way the brain jointly encodes space and time.
