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Geert Litjens
Full Professor of AI for Medical Imaging in Radiology and Pathology
Geert Litjens is full professor of Artificial Intelligence for analysis of medical images in radiology and pathology at Radboud University Medical Center and co-chairs the Computation Pathology Group within the Diagnostic Image Analysis Group. His work focusses on application of modern machine learning methods to oncological pathology. Furthermore, he leads and particaptes in several research project bridging the gap between medical specialties such as in prostate and pancreatic cancer. Last, within the European BIGPICTURE project he leads the work package on artificial intelligence.
Education
PhD in Medical Sciences
Radboud University Medical Center
MSc in Biomedical Image Analysis
Eindhoven University of Technology
BSc in Biomedical Engineering
Eindhoven University of Technology
Recent Posts
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Recent Publications
(2022).
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
Nat Med.
(2021).
Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation..
Pathology.
(2021).
Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps..
Eur Urol Focus.
(2021).
Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology..
Toxicol Pathol.
(2021).
The Medical Segmentation Decathlon.
arXiv:2106.05735.
Recent Talks
Visit Dutch Prostate Cancer Patient Foundation
AI in medical imaging is often pitched as either an aid or a threat to the physician. But what is the impact for the patient? Can they benefit from the introduction of AI and if so, in which way? In this talk I will sketch the impact of AI on prostate cancer diagnosis from the perspective of the patient.
Artificial Intelligence in Prostate Cancer Diagnostics
Artificial Intelligence is starting a more important role in diagnostics, also in diagnostics of prostate cancer. In this talk I will sketch the current applications of AI and give directions for future use, also with respect to clinical research.
Applications of Machine Learning for Clinical Practice
Clinical pathology is at the forefront of a digital revolution. In addition to the digital workflow, pathologists will also come into contact with machine learning algorithms aimed at improving their diagnostic accuracy and efficiency. In this presentation I highlight some applications which will be among the first to see use in clinical practice.
Introduction to Deep Learning in Medical Imaging
This presentations aims to provide an introduction to the basics and fundamental concepts of deep learning in medical imaging.
Bessensap
Should doctors fear for their jobs due to the rise of AI? In this presentation I explained which aspects of their job will change and why they should not fear, but welcome the introduction of AI in healthcare.