Search Results for author: Maria Ines Meyer

Found 5 papers, 2 papers with code

An augmentation strategy to mimic multi-scanner variability in MRI

1 code implementation23 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.

Data Augmentation

Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data

no code implementations3 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.

Lesion Segmentation

Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors

no code implementations8 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.

Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

no code implementations10 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.

Color Constancy Data Augmentation +4

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