ABSTRACT: Aiming at the problems of intensity inhomogeneity, boundary blurring and noise interference in the segmentation of three-dimensional volume data (such as medical images and industrial CT ...
The hype we have been sold for the past few years has been overwhelming. Hype Correction is the antidote. Can I ask you a question: How do you feel about AI right now? Are you still excited? When you ...
Journal of Nuclear Medicine Technology August 2025, jnmt.125.269869; DOI: https://doi.org/10.2967/jnmt.125.269869 ...
Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States ...
ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
Submodular maximization is a significant area of interest in combinatorial optimization, with numerous real-world applications. A research team led by Xiaoming SUN from the State Key Lab of Processors ...
Driven by a Vision to Deliver Smarter, Longer-Lasting Energy Solutions, Flux Power Integrates AI and Software Intelligence to Meet the Growing Demand for Adaptive, Data-Driven Electrification The ...
Abstract: Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well ...
Please see our online documentation here. This document provides only a brief overview. As an additional resource a web interface is provided, including an example dataset, here. Expectation ...
If you use these materials for teaching or research, please use the following citation: Rhoads, S. A. (2023). pyEM: Expectation Maximization with MAP estimation in ...
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