452 citations · 676 across the 13 of their papers we have counts for
8 papers · 1 filter
Augmentation based unsupervised domain adaptation
Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen +5
The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well a…
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation
Marta B. M. Ranzini, Lucas Fidon, Sébastien Ourselin +2
In fetal Magnetic Resonance Imaging, Super Resolution Reconstruction (SRR) algorithms are becoming popular tools to obtain high-resolution 3D volume reconstructions from low-resolu…
Scale factor point spread function matching: Beyond aliasing in image resampling
M. Jorge Cardoso, Marc Modat, Tom Vercauteren +1
Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design. Conversely, in medical image resampling, images are consid…
Diffusion tensor driven image registration: a deep learning approach
Irina Grigorescu, Alena Uus, Daan Christiaens +6
Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to…
Interpretable Convolutional Neural Networks for Preterm Birth Classification
Irina Grigorescu, Lucilio Cordero-Grande, A David Edwards +3
The use of convolutional neural networks (CNNs) for classification tasks has become dominant in various medical imaging applications. At the same time, recent advances in interpret…
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre +9
Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this cha…