20 citations · 49 across the 7 of their papers we have counts for
8 papers
Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model?
Corentin Martens, Antonin Rovai, Daniele Bonatto +5
Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumours. However, severe limitations regarding the estimati…
XCycles Backprojection Acoustic Super-Resolution
Feras Almasri, Jurgen Vandendriessche, Laurent Segers +5
The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive result…
Initial condition assessment for reaction-diffusion glioma growth models: A translational MRI/histology (in)validation study
Corentin Martens, Laetitia Lebrun, Christine Decaestecker +8
Diffuse gliomas are highly infiltrative tumors whose early diagnosis and follow-up usually rely on magnetic resonance imaging (MRI). However, the limited sensitivity of this techni…
Voxelwise principal component analysis of dynamic [S-methyl-11C]methionine PET data in glioma patients
Corentin Martens, Olivier Debeir, Christine Decaestecker +6
Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosi…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Robust Perceptual Night Vision in Thermal Colorization
Feras Almasri, Olivier Debeir
Transforming a thermal infrared image into a robust perceptual colour Visible image is an ill-posed problem due to the differences in their spectral domains and in the objects' rep…