Beschreibung
This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.
Autorenporträt
Dr. Ying Cui is a scientist at Yahoo! Inc., her research is focused on information retrieval. She studied machine learning and data mining technology at Northeastern University. She is also affiliated to Massachusetts General Hospital and Harvard Medical School for applying learning techniques to medical research.
Herstellerkennzeichnung:
OmniScriptum SRL
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MD
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