Geert Litjens is an assistant professor of Computational Pathology at the Radboud University Medical Center. His research is at the intersection of machine learning, medical imaging, and oncology. He co-chairs the Computational Pathology Group, which develops automated machine learning systems for cancer detection, biomarker discovery and quantification, and improved prognostication.

He is also the developer of the ASAP software package for analyzing and visualizing whole-slide images and (co-)organizer of several high-profile challenges in medical imaging such as PROMISE12, CAMELYON, and PANDA. He (co-)authored over 60 publications in medical, imaging, and machine learning conferences and journals.


  • Computational Pathology
  • Machine Learning
  • Medical Imaging
  • Oncology


  • PhD in Medical Sciences, 2015

    Radboud University Medical Center

  • MSc in Biomedical Image Analysis, 2010

    Eindhoven University of Technology

  • BSc in Biomedical Engineering, 2006

    Eindhoven University of Technology



Assistant Professor

Radboud University Medical Center

May 2016 – Present Nijmegen, the Netherlands
Assistant professor in computation pathology

Postdoctoral Researcher

University of Heidelberg

May 2015 – April 2016 Heidelberg, Germany
Research projects on prostate cancer immunotherapy and lung adenocarcinoma subtyping.

Postdoctoral Researcher

Radboud University Medical Center

January 2015 – April 2015 Nijmegen, the Netherlands
Research projects on automated breast cancer metastases detection in lymph nodes and prostate cancer segmentation in biopsies using deep learning.

Grants & Awards

KNAW Early Career Award

  • Organisation: KNAW
  • Year: 2020
  • Amount: 15 k€

Radboud Institute for Health Sciences Junior Researcher

  • Organisation: RIHS
  • Year: 2019
  • Amount: 250 k€

Radboud Science Award

StITpro Pathologie II Grant

  • Organisation: StITPro
  • Year: 2017
  • Amount: 220 k€

Veni Grant

  • Organisation: NWO
  • Year: 2017
  • Amount: 250 k€

Bas Mulder / Young Investigator Grant

Radboud Institute of Health Sciences Best PhD Thesis - Runner-up

Alexander von Humboldt Postdoctoral Fellowship

Robert F. Wagner Best Student Paper Award - Runner-up

  • Organisation: SPIE
  • Year: 2014

MICCAI Student Travel Award

  • Organisation: MICCAI
  • Year: 2012

Organized Scientific Meetings

Medical Imaging meets NeurIPS Workshop

  • Roles: Program Committee Member
  • Year: 2018, 2019

ISUP Gleason Grading Consensus Panel

  • Roles: Member
  • Year: 2019

MIDL (Medical Imaging with Deep Learning) Conference

  • Roles: Program Chair, Program Committee Member
  • Year: 2018 (PC), 2019, 2020 (PCM)

SPIE Medical Imaging

  • Role: Program Committee
  • Year: 2018, 2019, 2020

Computation Pathology Symposium at the European Conference of Pathology

  • Roles: Organizing Committee Member
  • Year: 2016 - now

CAMELYON16/17 Challenge Workshops

  • Roles: Organizer
  • Year: 2016, 2017

PROMISE12 Challenge Workshop

  • Roles: Organizer
  • Year: 2012

Invited Lectures

Bayer Prostate Cancer Academy

  • Location: Baarn, the Netherlands
  • Year: 2019

Swedish Deep Learning Conference

  • Location: Norrköping, Sweden
  • Year: 2019

Nordic Pathology Symposium

  • Location: Linköping, Sweden
  • Year: 2019

NWO Bessensap

  • Location: Amsterdam, the Netherlands
  • Year: 2018

Dutch Pathology Days

  • Location: Ede, the Netherlands
  • Year: 2018

Pathology Visions Conference

  • Location: San Diego, United States of America
  • Year: 2017

Congress Dutch Pediatric Medicine Society

  • Location: Arnhem, the Netherlands
  • Year: 2017


  • Location: Melbourne, Australia
  • Year: 2017

MICCAI Medical Computer Vision Workshop

  • Location: Athens, Greece
  • Year: 2016

MICCAI Computational Precision Medicine Workshop

  • Location: Athens, Greece
  • Year: 2016

Digital Pathology Congress

  • Location: London, United Kingdom
  • Year: 2016

European Conference of Pathology - Computational Pathology Symposium

  • Location: Cologne, Germany
  • Year: 2016



Extensive experience with Python for data science and machine learning. Often used package include Numpy, Scipy, Tensorflow, Theano, and Matplotlib.


I used C++ for the development of image analysis algorithms using ITK and MeVisLab. In addition, ASAP is an open source Qt-based C++ application, developed for both Windows and Linux.


All software and algorithms I develop are under version control. Nowadays, we make extensive use of Git and Github for source code management.


Since 2014 my research has focused on challenges in diagnostic pathology. This has helped me attain knowledge on pathologist’s workflows in many applications, with emphasis on those in oncology.


During my MSc and PhD theses I worked at a major radiology hardware vendor and at a radiology department. I have some experience with all imaging modalities common in radiology, but most expertise with different types of MR imaging.

Data Analysis & Statistics

I have taken several (bio-)statistics courses and have working experience with advanced statistical methods. Examples include survival analysis and mixed models.

Deep Learning

Since 2013 I have been developing algorithms based on deep learning, initially with Theano, later with TensorFlow. My initial work and that of many others is covered in our [survey]({{< ref “/publication/litj-17/” >}}).

Medical Image Analysis

I have been working on image processing tools for over a decade and have applied, among others, image registration, filtering, and morphological image processing in different projects.


Dutch: native
English: fluent
German: intermediate
French: basic