Photo of Jeroen van der Laak

Jeroen van der Laak

Visiting Professor

My research aims to improve cancer diagnostics and prognostics using machine learning techniques and large data sets in Pathology.

Deep learning 

Advances in tissue slide digitization and machine learning have propelled computational pathology research. Especially the use of 'deep learning' techniques, trained with large numbers of histopathology images, has been shown to be very powerful.

Today, computer systems approach the level of humans for certain well-defined tasks in pathology. Examples are counting of mitoses for breast cancer grading and detection of lymph node metastases for tumour staging. 

My research focuses on development of such deep learning algorithms. The aims are twofold:

  1. to support the pathologists' work by increasing efficiency and reducing observer bias;
  2. to identify potential new (prognostic and predictive) biomarkers to aid personalized treatment.

To be able to reach these, a number of eminent challenges still exist. An important prerequisite for development of deep learning algorithms is the availability of (both high quality and high quantity) data. A large part of the research is therefore directed at establishing collaborations, acquiring clinical data as well as human tissues, and working with expert pathologists.

Next, research into different deep learning strategies is required to develop the most optimal models.

Lastly, developed models have to be validated in routine clinical practice, to prove safety and usability.

My research aims to focus on all these different aspects, with the final aim of improving cancer diagnostics and prognostics.

Publications

2024

Jasper Linmans, Gabriel Raya, Jeroen van der Laak, Geert Litjens (2024) Diffusion models for out-of-distribution detection in digital pathology Medical Image Analysis, Vol. 93, Article 103088 Continue to DOI
Jasper Linmans, Emiel Hoogeboom, Jeroen van der Laak, Geert Litjens (2024) The Latent Doctor Model for Modeling Inter-Observer Variability IEEE journal of biomedical and health informatics, Vol. 28, p. 343-354 Continue to DOI

2023

Marloes A. Smit, Francesco Ciompi, John-Melle Bokhorst, Gabi W. van Pelt, Oscar G.F. Geessink, Hein Putter, Rob A.E.M. Tollenaar, J. Han J.M. van Krieken, Wilma E. Mesker, Jeroen van der Laak (2023) Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment Journal of Pathology Informatics, Vol. 14, Article 100191 Continue to DOI
John-Melle Bokhorst, Iris D. Nagtegaal, Inti Zlobec, Heather Dawson, Kieran Sheahan, Femke Simmer, Richard Kirsch, Michael Vieth, Alessandro Lugli, Jeroen van der Laak, Francesco Ciompi (2023) Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer Cancers, Vol. 15, Article 2079 Continue to DOI
Jasper Linmans, Stefan Elfwing, Jeroen van der Laak, Geert Litjens (2023) Predictive uncertainty estimation for out-of-distribution detection in digital pathology Medical Image Analysis, Vol. 83 Continue to DOI

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