Search Results for author: Thomas Varsavsky

Found 13 papers, 0 papers with code

Augmentation based unsupervised domain adaptation

no code implementations23 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.

Anomaly Detection Unsupervised Domain Adaptation

Acquisition-invariant brain MRI segmentation with informative uncertainties

no code implementations7 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses.

MRI segmentation

The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

no code implementations4 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases.

Brain Segmentation

Test-time Unsupervised Domain Adaptation

no code implementations5 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).

Unsupervised Domain Adaptation

Hierarchical brain parcellation with uncertainty

no code implementations16 Sep 2020 Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

no code implementations MIDL 2019 Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark Graham, Parashkev Nachev, Sebastien Ourselin, Carole H. Sudre, M. Jorge Cardoso

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.

Let's agree to disagree: learning highly debatable multirater labelling

no code implementations4 Sep 2019 Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Ryutaro Tanno, Lorna Smith, Sébastien Ourselin, Rolf H. Jäger, M. Jorge Cardoso

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time.

object-detection Object Detection

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

no code implementations21 Aug 2019 Kerstin Kläser, Thomas Varsavsky, Pawel Markiewicz, Tom Vercauteren, David Atkinson, Kris Thielemans, Brian Hutton, M. Jorge Cardoso, Sebastien Ourselin

Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69. 68HU) compared to a baseline CNN (66. 25HU), but lead to significant improvement in the PET reconstruction - 115a. u.

Imitation Learning

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

no code implementations16 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).

Domain Adaptation

As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

no code implementations25 Jul 2019 Zach Eaton-Rosen, Thomas Varsavsky, Sebastien Ourselin, M. Jorge Cardoso

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions.

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

no code implementations21 Dec 2018 Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Lorna Smith, H. Rolf Jäger, M. Jorge Cardoso

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution.

PIMMS: Permutation Invariant Multi-Modal Segmentation

no code implementations17 Jul 2018 Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso

In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality.

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