Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification

Autor: Logan, Y., Kokilepersaud, K., Kwon, G., AlRegib, G., Wykoff, C., Yu, H.

In this paper, we propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography (OCT) using multi-modal learning. To demonstrate the effectiveness of this approach, we create a medical diagno

Externí odkaz: http://arxiv.org/abs/2203.10622

UNIQUE: Unsupervised Image Quality Estimation

Autor: Temel, D., Prabhushankar, M., AlRegib, G.

Publikováno v: D. Temel, M. Prabhushankar and G. AlRegib, "UNIQUE: Unsupervised Image Quality Estimation," in IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1414-1418, Oct. 2016

In this paper, we estimate perceived image quality using sparse representations obtained from generic image databases through an unsupervised learning approach. A color space transformation, a mean subtraction, and a whitening operation are used to e

Externí odkaz: http://arxiv.org/abs/1810.06631

Stochastic surprisal: An inferential measurement of free energy in neural networks.

Autor: Prabhushankar M; Omni Lab for Intelligent Visual Engineering and Science (OLIVES), Georgia Institute of Technology, Electrical and Computer Engineering, Atlanta, GA, United States., AlRegib G; Omni Lab for Intelligent Visual Engineering and Science (OLIVES), Georgia Institute of Technology, Electrical and Computer Engineering, Atlanta, GA, United States.

Publikováno v: Frontiers in neuroscience [Front Neurosci] 2023 Mar 14; Vol. 17, pp. 926418. Date of Electronic Publication: 2023 Mar 14 (Print Publication: 2023).

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