Ghent University Academic Bibliography

Your filters: cql: keyword exact "Radiomics"

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Artificial intelligence with deep learning in nuclear medicine and radiology

Milan Decuyper (UGent) , Jens Maebe (UGent) , Roel Van Holen (UGent) and Stefaan Vandenberghe (UGent)

(2021) EJNMMI PHYSICS. 8(1).
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A literature review of the potential diagnostic biomarkers of head and neck neoplasms

Heleen Konings, Sofie Stappers, Margot Geens, Benedicte Y. De Winter, Kevin Lamote (UGent) , Jan Van Meerbeeck (UGent) , Pol Specenier, Olivier M. Vanderveken and Kristien J. Ledeganck

(2020) FRONTIERS IN ONCOLOGY. 10.
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Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients : evaluation of the added prognostic value for overall survival and locoregional recurrence

Janna E van Timmeren, Wouter van Elmpt, Ralph TH Leijenaar, Bart Reymen, René Monshouwer, Johan Bussink, Leen Paelinck (UGent) , Evelien Bogaert (UGent) , Carlos De Wagter (UGent) , ELHASEEN ELAMIN (UGent) , et al.

(2019) RADIOTHERAPY AND ONCOLOGY. 136. p.78-85
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Binary Glioma grading : radiomics versus pre-trained CNN features

Milan Decuyper (UGent) , Stijn Bonte and Roel Van Holen (UGent)

(2018) Medical Imaging Summer School 2018 : Medical Imaging meets Deep Learning. In MISS 2018: poster session booklet p.13-13
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- Conference Paper
- C3
Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach

Stijn Bonte, Ingeborg Goethals (UGent) and Roel Van Holen (UGent)

(2016) Neuro-Oncology. 18(Supplement 4). p.iv38-iv38

Academic Bibliography

Artificial intelligence with deep learning in nuclear medicine and radiology

A literature review of the potential diagnostic biomarkers of head and neck neoplasms

Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients : evaluation of the added prognostic value for overall survival and locoregional recurrence

Binary Glioma grading : radiomics versus pre-trained CNN features

Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach

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