no code implementations • 23 Feb 2022 • Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen, Mads Nielsen, Akshay Pai, Sebastien Ourselin, Marc Modat, M Jorge Cardoso
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 as abnormality detection and segmentation.
1 code implementation • 14 Feb 2022 • Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Jørgensen, Ole Winther, Alfiia Galimzianova
We assess the explanation performance in terms of identification and localization by comparing model-selected with dermatologist-selected explanations, and gradient-weighted class-activation maps with dermatologist explanation maps, respectively.
no code implementations • 5 Oct 2020 • Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre, Mark S. Graham, Parashkev Nachev, M. Jorge Cardoso
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).
no code implementations • 16 Aug 2019 • Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
no code implementations • 16 Aug 2019 • Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
no code implementations • 20 Aug 2018 • Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai
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 which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.