93 citations · 145 across the 6 of their papers we have counts for
7 papers
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
Jeroen Bertels, David Robben, Dirk Vandermeulen +1
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the simi…
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Sofie Tilborghs, Ine Dirks, Lucas Fidon +19
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease.…
Optimization with soft Dice can lead to a volumetric bias
Jeroen Bertels, David Robben, Dirk Vandermeulen +1
Segmentation is a fundamental task in medical image analysis. The clinical interest is often to measure the volume of a structure. To evaluate and compare segmentation methods, the…
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
David Robben, Anna M. M. Boers, Henk A. Marquering +9
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the p…
Perfusion parameter estimation using neural networks and data augmentation
David Robben, Paul Suetens
Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making. Current perfusion analysis relies on deconvolution of the measured signals, an opera…