Interpreting Mass Spectrometry Images Using Stacked Learning
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Authors
Wang, Emerson
Date
Type
thesis
Language
eng
Keyword
DESI , Mass Spectrometry , Deep Learning , Stacked Learning , Atrial Fibrillation
Alternative Title
Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) allows for hyperspectral analysis of tissue samples non-destructively under ambient conditions. A downside of such a bountiful source of data is that the high dimensional images suffer from the curse of dimensionality, often requiring substantial pre-processing and complex algorithmic approaches to extract meaningful interpretations of the data. Postoperative atrial fibrillation (POAF) is a cardiac arrhythmia that can occur after heart surgery, resulting in poorer recovery and higher treatment costs. We proposed a stacked learning system using a combination of DESI-MS and patient demographic information to classify POAF in a set of patients. This stacked learning system was composed of three stages. First, a CNN was trained to identify nuclear hypertrophy in DESI-MS images.
Second, the CNN’s output was combined with patient demographic information and dimensionally reduced. Finally, a support vector machine was used to classify POAF on the resulting dataset. Our study showed that the full stacked learning system outperformed the individual components and also provided a preliminary step toward predicting POAF in patients.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
CC0 1.0 Universal
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
CC0 1.0 Universal