Name
Title | ||
|---|---|---|
Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISBI.2017.7950485 | 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) |
Keywords | DocType | Volume |
deep learning,radiomics,feature learning,hand-designed features,computed tomography,five-year mortality | Conference | abs/1607.00267 |
ISBN | Citations | PageRank |
978-1-5090-1173-5 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Carneiro | 1 | 292 | 27.63 |
| Luke Oakden-Rayner | 2 | 4 | 0.75 |
| Andrew P. Bradley | 3 | 2087 | 195.95 |
| Jacinto C. Nascimento | 4 | 396 | 40.94 |
| Lyle J. Palmer | 5 | 73 | 3.10 |