19 citations · 26 across the 6 of their papers we have counts for
8 papers
MAIS: Memory-Attention for Interactive Segmentation
Mauricio Orbes-Arteaga, Oeslle Lucena, Sabastien Ourselin +1
Interactive medical segmentation reduces annotation effort by refining predictions through user feedback. Vision Transformer (ViT)-based models, such as the Segment Anything Model…
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…
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga +3
Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagno…
Test-time Unsupervised Domain Adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre +3
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain…
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…
Knowledge distillation for semi-supervised domain adaptation
Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen +5
In the absence of sufficient data variation (e.g., scanner and protocol variability) in annotated data, deep neural networks (DNNs) tend to overfit during training. As a result, th…
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen +4
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on whi…