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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning And Radiomic Features To Predict Overall Survival Time For Glioblastoma Patients, Lina Chato, Shahram Latifi
Machine Learning And Radiomic Features To Predict Overall Survival Time For Glioblastoma Patients, Lina Chato, Shahram Latifi
Electrical & Computer Engineering Faculty Research
Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A …
An Introduction To Federated Learning And Its Analysis, Manjari Ganapathy
An Introduction To Federated Learning And Its Analysis, Manjari Ganapathy
UNLV Theses, Dissertations, Professional Papers, and Capstones
With the onset of the digital era, data privacy is one of the most predominant issues. Decentralized learning is becoming popular as the data can remain within local entities by maintaining privacy. Federated Learning is a decentralized machine learning approach, where multiple clients collaboratively learn a model, without sharing raw data. There are many practical challenges in solving Federated Learning, which include communication set up, data heterogeneity and computational capacity of clients. In this thesis, I explore recent methods of Federated Learning with various settings, such as data distributions and data variability, used in several applications. In addition, I, specifically, …