Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111306
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Type: Conference paper
Title: Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography
Author: Carneiro, G.
Oakden-Rayner, L.
Bradley, A.
Nascimento, J.
Palmer, L.
Citation: Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017 / vol.abs/1607.00267, pp.130-134
Publisher: IEEE
Publisher Place: Online
Issue Date: 2017
Series/Report no.: IEEE International Symposium on Biomedical Imaging
ISBN: 9781509011711
ISSN: 1945-7928
1945-8452
Conference Name: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)
Statement of
Responsibility: 
Gustavo Carneiro, Luke Oakden-Raynery, Andrew P. Bradley, Jacinto Nascimento, Lyle Palmer
Abstract: In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning models extended to 3-D inputs, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection and extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning models produces a mean 5-year mortality prediction AUC in [68.8%,69.8%] and accuracy in [64.5%,66.5%], while radiomics produces a mean AUC of 64.6% and accuracy of 64.6%. The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.
Keywords: Deep learning; radiomics; feature learning; hand-designed features; computed tomography; five-year mortality
Rights: ©2017 IEEE
RMID: 0030073140
DOI: 10.1109/ISBI.2017.7950485
Grant ID: http://purl.org/au-research/grants/arc/DP140102794
http://purl.org/au-research/grants/arc/FT110100623
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7944115
Appears in Collections:Computer Science publications

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