1 code implementation • 14 Jun 2022 • Moritz Roman Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning, Roland Wiest, Waldo Enrique Valenzuela Pinilla, Mauricio Reyes, Maria Ines Meyer, Sook-Lei Liew, Florian Kofler, Ivan Ezhov, David Robben, Alexander Hutton, Tassilo Friedrich, Teresa Zarth, Johannes Bürkle, The Anh Baran, Bjoern Menze, Gabriel Broocks, Lukas Meyer, Claus Zimmer, Tobias Boeckh-Behrens, Maria Berndt, Benno Ikenberg, Benedikt Wiestler, Jan S. Kirschke
The test dataset will be used for model validation only and will not be released to the public.
1 code implementation • 23 Mar 2021 • Maria Ines Meyer, Ezequiel de la Rosa, Nuno Barros, Roberto Paolella, Koen van Leemput, Diana M. Sima
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data.
no code implementations • 3 Feb 2020 • Mattias Billast, Maria Ines Meyer, Diana M. Sima, David Robben
A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.
no code implementations • 8 Nov 2019 • Maria Ines Meyer, Ezequiel de la Rosa, Koen van Leemput, Diana M. Sima
In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors.
no code implementations • 10 Mar 2017 • Adrian Galdran, Aitor Alvarez-Gila, Maria Ines Meyer, Cristina L. Saratxaga, Teresa Araújo, Estibaliz Garrote, Guilherme Aresta, Pedro Costa, A. M. Mendonça, Aurélio Campilho
Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants.