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https://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 / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 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 |
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: | Aurora harvest 8 Computer Science publications |
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