Gijs van Tulder

This is an incomplete list of publications with open access PDFs when available.

See my profile on Google Scholar or Semantic Scholar for a full list of papers.

Publications

• Unpaired, unsupervised domain adaptation assumes your domains are already similar.

  Gijs van Tulder and Marleen de Bruijne.

  Medical Image Analysis, July 2023.

  Paper (PDF) Open access version (Elsevier) Source code

• Generating Artificial Artifacts for Motion Artifact Detection in Chest CT.

  Guus van der Ham, Rudolfs Latisenko, Michail Tsiaousis, Gijs van Tulder.

  Presented at MICCAI 2022 SASHIMI workshop, September 2022.

  Paper (PDF) Paid version (Springer) Read online (SharedIt)

• Multi-view Analysis of Unregistered Medical Images Using Cross-View Transformers.

  Gijs van Tulder, Yao Tong, Elena Marchiori.

  Presented at MICCAI 2021, September 2021.

  Paper (PDF) Poster (PDF) Source code Paid version (Springer) Read online (SharedIt)

• Multi-Task Attention-Based Semi-supervised Learning for Medical Image Segmentation.

  Shuai Chen, Gerda Bortsova, Antonio Garcia-Uceda Juarez, Gijs van Tulder, Marleen de Bruijne.

  MICCAI 2019.

  Paid version (Springer) Read online (SharedIt) ArXiv

• Learning Cross-Modality Representations from Multi-Modal Images.

  Gijs van Tulder and Marleen de Bruijne.

  IEEE Transactions on Medical Imaging, September 2018.

  Paper (PDF) Paid version (IEEE)

• Representation Learning for Cross-Modality Classification.

  Gijs van Tulder and Marleen de Bruijne.

  Presented at the MICCAI 2016 Workshop on Medical Computer Vision, October 2016.

  Paper (PDF) Poster (PDF) Paid version (Springer) Read online (SharedIt)

• Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks.

  Gerda Bortsova, Gijs van Tulder, Florian Dubost, Tingying Peng, Nassir Navab, Aad van der Lugt, Daniel Bos, Marleen de Bruijne.

  MICCAI 2016.

  Official version (Springer) Read online (SharedIt) ArXiv

• Combining Generative and Discriminative Representation Learning for Lung CT Analysis with Convolutional Restricted Boltzmann Machines.

  Gijs van Tulder and Marleen de Bruijne.

  IEEE Transactions on Medical Imaging, May 2016.

  Paper (PDF) Source code Paid version (IEEE)

• Theano: A Python framework for fast computation of mathematical expressions.

  The Theano Development Team.

  ArXiv, May 2016.

  Paper (PDF) ArXiv

• Why Does Synthesized Data Improve Multi-sequence Classification?

  Gijs van Tulder and Marleen de Bruijne.

  Oral presentation at MICCAI 2015, October 2015.

  Paper (PDF) Poster (PDF) Paid version (Springer) Read online (SharedIt)

• Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine.

  Gijs van Tulder and Marleen de Bruijne.

  Presented at the MICCAI 2014 Workshop on Medical Computer Vision, September 2014.

  Paper (PDF) Poster (PDF) Paid version (Springer) Read online (SharedIt)

• Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features.

  Babak Loni, Gijs van Tulder, Pascal Wiggers, David Tax, Marco Loog.

  Text, Speech and Dialogue, 2011.

  Paper (PDF) Paid version (Springer) Read online (SharedIt)

Links

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