Romaszko, L., Lazarus, A., Gao, H. , Borowska, A. , Luo, X. and Husmeier, D. (2019) Massive Dimensionality Reduction for the Left Ventricular Mesh. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 24. ISBN 9781927877647 (doi: 10.11159/icsta19.24)
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Abstract
Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Romaszko, Mr Lukasz and Luo, Professor Xiaoyu and Husmeier, Professor Dirk and Borowska, Dr Agnieszka and Gao, Dr Hao and Lazarus, Dr Alan |
Authors: | Romaszko, L., Lazarus, A., Gao, H., Borowska, A., Luo, X., and Husmeier, D. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Mathematics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
ISSN: | 2562-7767 |
ISBN: | 9781927877647 |
Copyright Holders: | Copyright © 2019 International ASET Inc. |
First Published: | First published in Proceedings of the International Conference on Statistics: Theory and Applications (ICSTA’19): 24 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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