An interdisciplinary, inter-university seminar series
The seminar can be attended at this fixed zoom link.
Medical institutions around the world are adopting machine learning (ML) systems to assist in analyzing health data; at the same time, the fairness research community has shown that ML systems can be biased, resulting in disparate performance for specific subpopulations. In this talk, we will discuss the relationship between bias, ML, and health systems, addressing the specific case of gender bias in X-ray classifiers for computer-assisted diagnosis.
Dr. Ferrante received his Systems Engineering degree from UNICEN University (Argentina), completed his PhD in Computer Sciences at Université Paris-Saclay and INRIA in Paris, France, and worked as a postdoctoral researcher at Imperial College London in the UK. He returned to Argentina in 2017, where he holds a permanent researcher position from the Argentina's National Research Council (CONICET). He leads the Machine Learning for Biomedical Image Computing research line in the Research Institute for Signals, Systems and Computational Intelligence, sinc(i). In 2020 Dr. Ferrante received the Young Researcher Award from the National Academy of Sciences of Argentina, and the Mercosur Science & Technology Award for his scientific contributions in the area of AI for medical image computing. His research interests span both artificial intelligence and biomedical image analysis, with a focus on deep learning methods.
This will be a hybrid seminar: you can attend both via Zoom (link above) or physically. Mr. Ferrante will hold his presentation virtually, yet it is also possible (and very much encouraged!) to attend the seminar physically in room 170, DTU building 324. We will have coffee and discussions together before (starting 14:30) and after the talk. If you plan on coming to the physical meeting, please send a brief, informal message to Eike Petersen, so that we can switch to a larger room should it be necessary.
Friday October 1 2021 13:00-14:00 Julia Amann (ETH): Reconciling medical AI & patient-centered care
Friday September 17 2021 11:00-12:00 Anders Eklund (Linköping University): Sharing synthetic medical images - a way to circumvent GDPR? [Video]
Friday June 18 2021, 13:00-14:00 Jens Chistian Krarup Bjerring, (AU): Black-box decision making in medicine: some thoughts and questions [Video]
Friday May 28 2021 13:00-14:00 Veronika Cheplygina (ITU): How I failed machine learning in medical imaging -- shortcomings and recommendations [Slides] [Video]
Friday May 7 2021 15:00-16:00 Lars Kai Hansen (DTU): Values in AI [Slides] [Video]
Responsible AI draws on widely different scientific disciplines, from the technical aspects of AI, via ethics, philosophy and law, to the individual realities of different application domains. We wish to take advantage of the limitations imposed by Covid-19 to start an informal conversation across Denmark about different aspects of Responsible AI via a hybrid format seminar series. We hope to catch your interest with three seminar talks before the summer vacation, following which we hope to merge efforts across universities to start a truly inter-university seminar series.
Please join us for exciting talks and discussions.