This post is the first of a three post series on using deep learning to tackle the CAMELYON Challenge. This first post covers basic convolutional neural network training using the PatchCAMELYON dataset and TensorFlow 2.0.

In 2018 Jeroen van der Laak and I were nominated and eventually won a Radboud Science Award for our work with the CAMELYON challenge. One part of the prize was the oppertunity to turn our research into an educational program for elementary schools, specifically ages 9 till 12. This post is a summary of my experience going through this process and showcases the things we developed.

Interview together with Wouter Bulten on our publication in JAMA Oncology on using AI for automated Gleason grading.

Interview in the NOS op 3 tech podcast on AI for medical imaging

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 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.

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.