IEEE Transactions on

Medical Imaging

Dr. Karim Lekadir has been an Associate Editor for IEEE-TMI since 2017, having managed the review of over 100 papers on several research topics of medical image analysis. These include in particular deep learning based medical image segmentation and computer-aided diagnosis in the fields of cardiovascular, brain, bone and cancer imaging. As an Associate Editor, Karim’s tasks include reading the submitted papers, selecting and inviting the best possible reviewers for each manuscript depending on the specific topic, and making recommendations to the Editor-in-Chief based on the received reviews.

As a researcher, Karim is Director of the Artificial Intelligence in Medicine Lab at the University of Barcelona (BCN-AIM). He did his undergraduate studies in mathematics and computing in France, then obtained a PhD in medical image computing from Imperial College London, where he developed a commercial software for cardiac image quantification that is used in more than 250 clinical centres worldwide. He was also a postdoctoral researcher at Stanford University, where he developed deep learning approaches for characterising atherosclerotic plaque constituents in carotid ultrasound. Currently, his research focuses on the development of data science and machine learning approaches for the analysis of large-scale biomedical data, including medical imaging. For instance, he is the Scientific Coordinator of the euCanSHare project funded by the European Commission, that is building a big data platform for cardiovascular research. Furthermore, he is the Scientific Coordinator of the EuCanImage project, also funded by the European Commission, that will develop integrative artificial intelligence solutions from cancer imaging and non-imaging data.

Dr. Bernhard Kainz has been an Associate Editor since 2019 and primarily oversees manuscript submissions in the areas of machine learning for imaging.

Dr. Kainz has been working with human-centred computing methods since 2007 when he was doing a Ph.D. at Graz University of Technology. His research is about intelligent algorithms in healthcare, especially Medical Imaging. Dr. Kainz is working on self-driving medical image acquisition that can guide human operators in real-time during diagnostics. More recently he became interested in normative learning and its translation to population screening.

Since 2015 he has been a faculty member at the Department of Computing at Imperial College London, heading the human-in-the-loop computing group. He is one of four academics leading the Biomedical Image Analysis, BioMedIA Collaboratory, Affordable Imaging stream lead for the EPSRC Centre for Doctoral Training in Smart Medical Imaging and deputy director of the UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare.

Dr. Kainz has been an appreciative member of the IEEE throughout his career. He is an IEEE Senior Member working with IEEE journals and conferences since over a decade.