Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data
Joint Authors
Papastergiou, Thomas
Zacharaki, Evangelia I.
Megalooikonomou, Vasileios
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Multidimensional data that occur in a variety of applications in clinical diagnostics and health care can naturally be represented by multidimensional arrays (i.e., tensors).
Tensor decompositions offer valuable and powerful tools for latent concept discovery that can handle effectively missing values and noise.
We propose a seamless, application-independent feature extraction and multiple-instance (MI) classification method, which represents the raw multidimensional, possibly incomplete, data by means of learning a high-order dictionary.
The effectiveness of the proposed method is demonstrated in two application scenarios: (i) prediction of frailty in older people using multisensor recordings and (ii) breast cancer classification based on histopathology images.
The proposed method outperforms or is comparable to the state-of-the-art multiple-instance learning classifiers highlighting its potential for computer-assisted diagnosis and health care support.
American Psychological Association (APA)
Papastergiou, Thomas& Zacharaki, Evangelia I.& Megalooikonomou, Vasileios. 2018. Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data. Complexity،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1136303
Modern Language Association (MLA)
Papastergiou, Thomas…[et al.]. Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data. Complexity No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1136303
American Medical Association (AMA)
Papastergiou, Thomas& Zacharaki, Evangelia I.& Megalooikonomou, Vasileios. Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data. Complexity. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1136303
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136303