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|Title:||Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography|
|Citation:||Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017 / vol.abs/1607.00267, pp.130-134|
|Series/Report no.:||IEEE International Symposium on Biomedical Imaging|
|Conference Name:||IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)|
|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|
|Appears in Collections:||Computer Science publications|
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