Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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977 Locations available

693.932 PEOPLE
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Naji, M.
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King's College London

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (10/10 displayed)

  • 2023An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field11citations
  • 2022Transformer-based out-of-distribution detection for clinically safe segmentationcitations
  • 2022Automated Koos Classification of Vestibular Schwannoma12citations
  • 2021A population-based study of head injury, cognitive function and pathological markers9citations
  • 2021Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction15citations
  • 2019Enhancing photoacoustic visualization of medical devices with elastomeric nanocomposite coatings6citations
  • 2019Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy78citations
  • 2018LED-based photoacoustic imaging of medical devices with carbon nanotube-polydimethylsiloxane composite coatings1citations
  • 2018Short Acquisition Time PET/MR Pharmacokinetic Modelling Using CNNs5citations
  • 2011An MRI based workflow for prostate radiation therapy planningcitations

Places of action

Chart of shared publication
Payette, Kelly
1 / 1 shared
Hutter, Jana
1 / 1 shared
Hall, Megan
1 / 1 shared
Zampieri, Carla Avena
1 / 1 shared
Verdera, Jordina Aviles
1 / 1 shared
Hajnal, Joseph
1 / 1 shared
Deprez, Maria
1 / 1 shared
Rutherford, Mary
1 / 1 shared
Story, Lisa
1 / 1 shared
Uus, Alena
1 / 1 shared
Tomi-Tricot, Raphael
1 / 1 shared
Tudosiu, Petru-Daniel
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Teo, James
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Jean-Marie, U.
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Mah, Yee
1 / 1 shared
Graham, Mark
1 / 1 shared
Cardoso, M. Jorge
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Nachev, Parashkev
1 / 3 shared
Jäger, Rolf H.
1 / 1 shared
Werring, David
1 / 1 shared
Pinaya, Walter Hl
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Wright, Paul
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Paddick, Ian
1 / 1 shared
Kitchen, Neil
1 / 1 shared
Vercauteren, Tom
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Shapey, Jonathan
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Kujawa, Aaron
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Okasha, Mohamed
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Connor, Steve
1 / 1 shared
Oviedova, Anna
1 / 1 shared
Grishchuk, Diana
1 / 1 shared
Schott, Jonathan M.
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Li, Peichao
1 / 1 shared
Horgan, Conor
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Bahl, Anisha
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Ebner, Michael
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Noonan, Philip
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Noimark, Sacha
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Singh, Mithun Kuniyil Ajith
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Xia, Wenfeng
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Brown, Nina Montana
1 / 1 shared
Maneas, Efthymios
2 / 2 shared
West, Simeon J.
2 / 3 shared
Desjardins, Adrien E.
2 / 4 shared
Scott, Catherine J.
1 / 1 shared
Jiao, Jieqing
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Kläser, Kerstin
1 / 1 shared
Markiewicz, Pawel J.
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Melbourne, Andrew
1 / 1 shared
Hutton, Brian F.
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Hughes, Cynthia
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Salvado, Olivier
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Parker, Joel
1 / 1 shared
Denham, James
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Fisher, Kristen
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Wratten, Chris
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Lau, Peter
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Capp, Anne
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Ebert, Martin
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Lambert, Jonathon
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Fripp, Jurgen
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Patterson, Jacqueline
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Greer, Peter
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Chart of publication period
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Co-Authors (by relevance)

  • Payette, Kelly
  • Hutter, Jana
  • Hall, Megan
  • Zampieri, Carla Avena
  • Verdera, Jordina Aviles
  • Hajnal, Joseph
  • Deprez, Maria
  • Rutherford, Mary
  • Story, Lisa
  • Uus, Alena
  • Tomi-Tricot, Raphael
  • Tudosiu, Petru-Daniel
  • Teo, James
  • Jean-Marie, U.
  • Mah, Yee
  • Graham, Mark
  • Cardoso, M. Jorge
  • Nachev, Parashkev
  • Jäger, Rolf H.
  • Werring, David
  • Pinaya, Walter Hl
  • Wright, Paul
  • Paddick, Ian
  • Kitchen, Neil
  • Vercauteren, Tom
  • Shapey, Jonathan
  • Kujawa, Aaron
  • Okasha, Mohamed
  • Connor, Steve
  • Oviedova, Anna
  • Grishchuk, Diana
  • Schott, Jonathan M.
  • Li, Peichao
  • Horgan, Conor
  • Bahl, Anisha
  • Ebner, Michael
  • Noonan, Philip
  • Noimark, Sacha
  • Singh, Mithun Kuniyil Ajith
  • Xia, Wenfeng
  • Brown, Nina Montana
  • Maneas, Efthymios
  • West, Simeon J.
  • Desjardins, Adrien E.
  • Scott, Catherine J.
  • Jiao, Jieqing
  • Kläser, Kerstin
  • Markiewicz, Pawel J.
  • Melbourne, Andrew
  • Hutton, Brian F.
  • Hughes, Cynthia
  • Salvado, Olivier
  • Parker, Joel
  • Denham, James
  • Fisher, Kristen
  • Wratten, Chris
  • Lau, Peter
  • Capp, Anne
  • Ebert, Martin
  • Lambert, Jonathon
  • Fripp, Jurgen
  • Patterson, Jacqueline
  • Greer, Peter
OrganizationsLocationPeople

document

Transformer-based out-of-distribution detection for clinically safe segmentation

  • Tudosiu, Petru-Daniel
  • Teo, James
  • Ourselin, Sebastien
  • Jean-Marie, U.
  • Mah, Yee
  • Graham, Mark
  • Cardoso, M. Jorge
  • Nachev, Parashkev
  • Jäger, Rolf H.
  • Werring, David
  • Pinaya, Walter Hl
  • Wright, Paul
Abstract

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model's segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data.

Topics
  • impedance spectroscopy