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.



Roberto A. Leon-Ferre, Jodi M. Carter, David Zahrieh, Jason P. Sinnwell, Roberto Salgado, Vera J. Suman, David W. Hillman, Judy C. Boughey, Krishna R. Kalari, Fergus J. Couch, James N. Ingle, Maschenka Balkenhol, Francesco Ciompi, Jeroen van der Laak, Matthew P. Goetz (2024) Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer npj Breast Cancer, Vol. 10, Article 25 Continue to DOI
Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Shenghua Cheng, Jiabo Ma, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E. Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Xiuli Liu, Nasir Rajpoot, Mitko Veta, Francesco Ciompi (2024) LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset IEEE journal of biomedical and health informatics, Vol. 28, p. 1161-1172 Continue to DOI
Khrystyna Faryna, Jeroen van der Laak, Geert Litjens (2024) Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology Computers in Biology and Medicine, Vol. 170, Article 108018 Continue to DOI
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