Talk at NVPHBV Spring Meeting - "Machine learning for medical imaging: shortcomings and recommendations"
A talk given by Veronika Cheplygina at the NVPHBV Spring Meeting in Zwolle, The Netherlands, on 18th May 2022.
Responsible research practices for machine learning in medical imaging
Medical imaging is an important research field with many opportunities for improving patients' health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such as optimizing for publication. In this talk I discuss several problems which occur when we as researchers make decisions about choosing datasets, methods, evaluation metrics, and publication strategies. I will also discuss various initiatives that have already been started to counteract these problems, and provide some more general recommendations on how to further these address problems in the future.
Dr. Veronika Cheplygina's research focuses on limited labeled scenarios in machine learning, in particular in medical image analysis. She received her Ph.D. from Delft University of Technology in 2015. After a postdoc at the Erasmus Medical Center, in 2017 she started as an assistant professor at Eindhoven University of Technology. In 2020, failing to achieve various metrics, she left the tenure track of search of the next step where she can contribute to open and inclusive science. In 2021 she started as an associate professor at IT University of Copenhagen. Next to research and teaching, Veronika blogs about academic life at https://www.veronikach.com. She also loves cats, which you will often encounter in her work.