Researcher
Diana Sima
- Disciplines:Applied mathematics in specific fields, Computer architecture and networks, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences, Control systems, robotics and automation, Modelling, Design theories and methods, Mechatronics and robotics, Biological system engineering, Computer theory, Signal processing
Affiliations
- Dynamical Systems, Signal Processing and Data Analytics (STADIUS) (Division)
Member
From1 Aug 2020 → 31 Oct 2017 - ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics (Division)
Member
From15 Feb 2002 → 31 Oct 2017
Projects
1 - 2 of 2
- Machine Learning for Classifying Abnormal Brain Tissue Progression based on Multi-parametric Magnetic Resonance DataFrom7 Oct 2013 → 23 Oct 2017Funding: Own budget, for example: patrimony, inscription fees, gifts
- Advanced Signal Processing for Magnetic Resonance SpectroscopyFrom10 Jan 2008 → 29 Nov 2011Funding: Own budget, for example: patrimony, inscription fees, gifts
Publications
11 - 20 of 82
- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction(2018)
Authors: Bharath Halandur Nagaraja, Steven Colleman, Diana Sima, Sabine Van Huffel
Pages: 463 - 473 - Machine Learning for Classifying Abnormal Brain Tissue Progression based on Multi-parametric Magnetic Resonance Data(2017)
Authors: Adrian Ion - Margineanu, Sabine Van Huffel, Frederik Maes, Diana Sima
Number of pages: 170 - Age-dependent whole brain and grey matter annual atrophy rates in healthy adults(2017)
Authors: Diana Sima, T Billiet, T Vande Vyvere, A Maertens, D Smeets, W Van Hecke
Pages: 559 - 560 - New and enlarging lesion location for different MS clinical phenotypes(2017)
Authors: Diana Sima, S Jain, E Roura, A Maertens, D Smeets, D Sappey-Marinier, F Durand-Dubief, W Van Hecke
Pages: 819 - 819 - A comparison of Machine Learning approaches for classifying Multiple Sclerosis courses using MRSI and brain segmentations(2017)
Authors: Adrian Ion - Margineanu, G Kocevar, Claudio Stamile, Diana Sima, F Durand-Dublief, Sabine Van Huffel, D Sappey-Marinier
Pages: 1 - 8 - A comparison of Machine Learning approaches for classifying Multiple Sclerosis courses using MRSI and brain segmentations(2017)
Authors: Claudio Stamile, Diana Sima, Sabine Van Huffel
Pages: 643 - 651 - The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization(2017)
Authors: Nicolas Sauwen, Bharath Halandur Nagaraja, Diana Sima, Frederik Maes, Uwe Himmelreich, Sabine Van Huffel
Pages: 1 - 17 - Machine learning approach for classifying Multiple Sclerosis courses by combining clinical data with lesion loads and Magnetic Resonance metabolic features(2017)
Authors: Diana Sima, Sabine Van Huffel
- Non-Negative Canonical Polyadic Decomposition for Tissue Type Differentiation in Gliomas(2017)
Authors: Bharath Halandur Nagaraja, Diana Sima, Nicolas Sauwen, Uwe Himmelreich, Lieven De Lathauwer, Sabine Van Huffel
Pages: 1124 - 1132 - Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization(2017)
Authors: Nicolas Sauwen, Diana Sima, Frederik Maes, Uwe Himmelreich, Sabine Van Huffel
Pages: 1 - 14