Binary Glioma grading : radiomics versus pre-trained CNN features
- Author
- Milan Decuyper (UGent) , Stijn Bonte and Roel Van Holen (UGent)
- Organization
- Abstract
- We compare the predictive performance of hand-engineered radiomics features with features extracted through a pre-trained CNN for discriminating glioblastoma from lower- grade glioma. The BRATS 2017 database was used containing MRI data of 285 patients. State-of-the-art performance was achieved (AUC of 96.4%) with radiomics features extracted from manually segmented tumour volumes. With pre-trained CNN features extracted from the tumour bounding box, an AUC of 93.5% was obtained.
- Keywords
- MRI, Radiomics, CNN, Glioma Grading
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8616460
- MLA
- Decuyper, Milan, et al. “Binary Glioma Grading : Radiomics versus Pre-Trained CNN Features.” Medical Imaging Summer School 2018 : Medical Imaging Meets Deep Learning, 2018, pp. 13–13.
- APA
- Decuyper, M., Bonte, S., & Van Holen, R. (2018). Binary Glioma grading : radiomics versus pre-trained CNN features. Medical Imaging Summer School 2018 : Medical Imaging Meets Deep Learning, 13–13.
- Chicago author-date
- Decuyper, Milan, Stijn Bonte, and Roel Van Holen. 2018. “Binary Glioma Grading : Radiomics versus Pre-Trained CNN Features.” In Medical Imaging Summer School 2018 : Medical Imaging Meets Deep Learning, 13–13.
- Chicago author-date (all authors)
- Decuyper, Milan, Stijn Bonte, and Roel Van Holen. 2018. “Binary Glioma Grading : Radiomics versus Pre-Trained CNN Features.” In Medical Imaging Summer School 2018 : Medical Imaging Meets Deep Learning, 13–13.
- Vancouver
- 1.Decuyper M, Bonte S, Van Holen R. Binary Glioma grading : radiomics versus pre-trained CNN features. In: Medical Imaging Summer School 2018 : Medical Imaging meets Deep Learning. 2018. p. 13–13.
- IEEE
- [1]M. Decuyper, S. Bonte, and R. Van Holen, “Binary Glioma grading : radiomics versus pre-trained CNN features,” in Medical Imaging Summer School 2018 : Medical Imaging meets Deep Learning, Favignana, Sicily, Italy, 2018, pp. 13–13.
@inproceedings{8616460,
abstract = {{We compare the predictive performance of hand-engineered radiomics features with features extracted through a pre-trained CNN for discriminating glioblastoma from lower- grade glioma. The BRATS 2017 database was used containing MRI data of 285 patients. State-of-the-art performance was achieved (AUC of 96.4%) with radiomics features extracted from manually segmented tumour volumes. With pre-trained CNN features extracted from the tumour bounding box, an AUC of 93.5% was obtained.}},
author = {{Decuyper, Milan and Bonte, Stijn and Van Holen, Roel}},
booktitle = {{Medical Imaging Summer School 2018 : Medical Imaging meets Deep Learning}},
keywords = {{MRI,Radiomics,CNN,Glioma Grading}},
language = {{eng}},
location = {{Favignana, Sicily, Italy}},
pages = {{13--13}},
title = {{Binary Glioma grading : radiomics versus pre-trained CNN features}},
url = {{http://iplab.dmi.unict.it/miss/posters.htm}},
year = {{2018}},
}