Prof Lena Maier-Hein (Heidelberg University) gave us a talk on ‘Intelligent Systems in Cancer Care‘
Professor Lena Maier-Hein from Heidelberg University gave us a talk on ‘Intelligent Systems in Cancer Care‘ on Tuesday 14th February 2pm | Lecture Theatre B, Department of Computer Science, Parks Road, Oxford.
Speaker bio:
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).
Abstract:
The breakthrough successes of deep learning-based solutions in various fields of research and practice have attracted a growing number of researchers to work in the field of medical image analysis. However, are we really solving the right problems? A key component of Intelligent medical systems is their perception. While the current state of the art in interventional healthcare largely relies on conventional imaging modalities, we challenge common practice by proposing intelligent medical systems based on novel biophotonics-based techniques that go beyond human perception with modern machine learning methods. Promising as they may appear, however, these systems can only be as ‘intelligent’ as the validation that was conducted on the algorithms used. Although validation is the basis for measuring all scientific progress as well as a key prerequisite for successful clinical translation, current common practice in the entire field of medical image analysis is heavily flawed, with strategies and metrics used frequently not reflecting the underlying medical problem. We challenge these shortcomings and propose solutions compiled by an international consortium of medical and machine learning experts from over 70 institutions worldwide.
