Speaker Highlight - Bram van Ginneken
About
Bram van Ginneken was born in Nuenen in 1970. He studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he obtained his PhD at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography, where he continued to work and set up a research group on medical image analysis. His PhD research on automated detection of tuberculosis resulted in medical device software called CAD4TB. With installations in over 75 countries worldwide, CAD4TB is the most widely used autonomous AI solution for the interpretation of medical images. In 2010, he moved to Radboud University Medical Center where he set up the Diagnostic Image Analysis Group and was appointed full professor in 2012. He (co-)authored over 300 publications in international journals. Since 2010, he also works for the Fraunhofer Institute for Digital Medicine MEVIS in Bremen, Germany. In 2014, he founded Thirona, a company that develops software for CT lung image analysis. He pioneered the concept of challenges in medical image analysis and created grand-challenge.org. In 2024, he founded Plain Medical, a company that develops AI solutions to reduce the workload of radiologists.
Medical image analysis should be really open, not only pretend to be open (as OpenAI)
We have passed a tipping point in medical image analysis. Analyzing medical images for the benefit of patients, once the exclusive domain of human specialists such as radiologists, can now be done by computers, thanks to the breakthrough of deep learning. This should have an effect on the research agenda of medical image analysis scientists. This new agenda is the topic of this talk. I will identify topics that we should focus less on, such as developing yet another variation of a segmentation method, and topics that should receive more attention, such as developing methods for efficient data annotation and benchmarking. Above all, I will argue that science should be much more open to have impact. I will show the devastating effect of keeping results closed. The section on code availability and data availability, nowadays mandatory in many journals, is usually the place where authors write down creative excuses why they do not share their code and data. This must change