IBIIS-AIMI Seminar: Missing the (Bench)mark? - Lena Maier Hein, PhD
Machine learning has begun to revolutionize almost all areas of health research. Success stories cover a wide variety of application fields ranging from radiology and gastroenterology all the way to mental health. Strikingly, however, solutions that perform favorably in research generally do not translate well to clinical practice, and little attention is being given to learning from failures. Focusing on biomedical image analysis as a key area of health-related machine learning, this talk will present pitfalls, caveats and recommendations related to machine learning-based biomedical image analysis. As a particular highlight, it will cover yet unpublished work on two key research questions related to biomedical image analysis competitions: 1) How can we best select performance metrics according to the characteristics of the driving biomedical question? And 2) Why is the winner the best? The results have been compiled based on the input of hundreds of image analysis researchers worldwide.
Lena Maier-Hein is a full professor at Heidelberg University (Germany) and managing director of the National Center for Tumor Diseases (NCT) Heidelberg. At the German Cancer Research Center (DKFZ) she is head of the division Intelligent Medical Systems (IMSY) and managing director of the "Data Science and Digital Oncology" cross-topic program. Her research concentrates on machine learning-based biomedical image analysis with a specific focus on surgical data science, computational biophotonics and validation of machine learning algorithms. She is a fellow of the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and of the European Laboratory for Learning and Intelligent Systems (ELLIS), president of the MICCAI special interest group on challenges and chair of the international surgical data science initiative.
Lena Maier-Hein serves on the editorial board of the journals Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence and Medical Image Analysis. During her academic career, she has been distinguished with several science awards including the 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) and the 2017/18 Berlin-Brandenburg Academy Prize. She has received a European Research Council (ERC) starting grant (2015-2020) and consolidator grant (2021-2026).