Track B – AI for Medicine
Tue, July 20, 2021
|8:30-9:00 CET – Opening (joint with Track A)|
|9:00-10:00 CET – Keynote 1 (joint with Track A)
Mihaela van der Schaar (University of Cambridge)
Mihaela van der Schaar is a Professor of Machine Learning, AI and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute, and a Chancellor’s Professor at UCLA. She has received numerous awards, including the Oon Prize on Preventative Medicine, a National Science Foundation CAREER Award, the IBM Exploratory Stream Analytics Innovation Award, and the Philips Make a Difference Award. Her work has led to 35 USA patents and 45+ contributions to international standards for which she received 3 ISO Awards. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine. She is founder and director of the Cambridge Centre for AI in Medicine.
Why medicine is creating exciting new frontiers for machine learning and AI
|10:00-13:00 CET – Course 1
Marco Lorenzi (Inria)
Marco Lorenzi is a tenured research scientist at Inria Sophia Antipolis. His research interest is in the development of statistical and machine learning methods for the analysis of large-scale and heterogeneous biomedical data. Current research topics include Bayesian modeling and uncertainty quantification, time-series analysis, latent variable models, and federated learning. He’s an editorial board member of Nature Scientific Reports (Neurology Panel) and he was awarded in 2015 with the second ex-aequo prize for the ERCIM Cor Baayen Award.
Federated learning methods and frameworks for collaborative data analysis
|14:30-17:00 CET – Course 2
Gaël Varoquaux (Inria)
Gaël Varoquaux is a tenured research director at Inria. His research focuses on statistical-learning tools for data science and scientific inference. Since 2008, he has been exploring data-intensive approaches for social and health sciences. More generally, he develops tools to make machine learning easier, with models suited for real-life, uncurated data. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. He has a PhD in quantum physics and is a graduate from Ecole Normale Superieure, Paris.
Dirty data science: machine learning on non-curated data
|17:30-19:00 CET – Participant posters and demos (joint with Track A)|
Wed, July 21, 2021
|9:00-10:00 CET – Keynote 2
Lena Maier-Hein (German Cancer Research Center - DKFZ)
Lena Maier-Hein received a Diploma (2005) and doctoral degree (Dr.- Ing. 2009) with distinction from Karlsruhe Institute of Technology and conducted her postdoctoral at the German Cancer Research Center (DKFZ) and at the Hamlyn Centre for Robotic Surgery at Imperial College London. During her time as junior group leader at the DKFZ (2011-2016), she finished her Habilitation (2013) at the University of Heidelberg. As DKFZ division head she is now working in the field of biomedical image analysis with a specific focus on surgical data science, computational biophotonics and validation of machine learning algorithms.
Why domain knowledge (still) matters in medical image analysis
|10:00-13:00 CET – Course 3
Thomas Moreau and Demian Wassermann (Inria)
Thomas Moreau is a researcher at Inria Saclay. His research interests are centered around unsupervised learning for time series, with application to neural activity recording such as MEG or fMRI. In particular, he has been working on Convolutional Dictionary Learning — studying both its computational aspects and its applications to pattern analysis — and on theoretical properties of learned optimization algorithms for inverse problems such as LISTA. He is also involved in Python open-source projects, mainly around parallel scientific computing. Before joining Inria, he obtained a PhD from ENS Cachan on convolutional representations for physiological signals.Demian Wassermann is a researcher at Inria since 2014. His research work is divided between the development and application of principled mathematical models for diffusion MRI micro and macro structure and the formalisation of human discourse to analyze neuroimaging data. For both these tasks, he harnesses the power of machine learning and artificial intelligence in probabilistic settings. Demian received a prestigious Starting Grant from the European Research Council (ERC) in 2017 for his project “NeuroLang”. Before joining Inria, he worked at the Harvard Medical School, specifically to the Brigham and Women’s Hospital.
Introduction to neuroimaging with Python
|14:30-17:00 CET – Course 4
Francesca Galassi (Inria) and Rutger Fick (Tribvn Healthcare)
Francesca Galassi is with the Empenn research team, Inria Rennes. Her research interests are in the fields of image processing, computer vision, and machine learning. She holds a Ph.D. in Biomedical Engineering from Imperial College London (UK), and completed a Post-doctoral fellowship in cardiovascular computational modeling at the Acute Vascular Imaging Unit in Oxford (UK). Her current research focuses on brain image processing, specifically segmentation and detection of white matter lesions from multi-modal MRI. Her teaching activity at the École supérieure d’ingénieurs de Rennes comprises Artificial Intelligence, Data Mining and Medical Imaging courses.Rutger Fick is a senior AI-specialized R&D engineer in medical imaging. His passion is to further the digitization of the hospital and healthcare industry, allowing medical specialists to focus on making high-level decisions and not waste time on repetitive, automatable tasks. His background encompasses three medical imaging domains: first a PhD on dMRI-based brain microstructure imaging at Inria Sophia Antipolis, followed by leading the development of CT-based radiotherapy treatment optimization algorithms at the award-winning start-up TheraPanacea. His current challenge is to improve the clinical workflow in digital pathology imaging by leading the design and development of interpretable-AI based algorithms at Tribvn Healthcare.
Towards domain-shift robustness in medical imaging analysis: A case study in 2D digital pathology and 3D brain MRI
|17:30-19:00 CET – Open discussion with industry
Boris Dimitrov (Check Point Cardio) and Nicklas Linz (ki elements)
Boris Dimitrov is the co-founder and CEO of Check Point Cardio. He is the founder of telemedicine in Bulgaria with 25 years of entrepreneurial experience. Check Point Cardio specializes in a wide range of medical services to provide excellent patient care. It developed one of the first systems for real-time online patient tele-monitoring with a fully operating cardiological medical center for continuous 24/7 observation, diagnostics, patient health management and emergency reaction services. Incepted by cardiologists, this disruptive technology offers quick and precise online diagnostics for preventive and emergency care.Nicklas Linz is the managing partner of ki elements, based in Saarbrücken, Germany. After completing his computer science studies at Saarland University, Nicklas worked as a scientist at DFKI in Prof. Wahlster’s research group Cognitive Assistance Systems. Here, he dealt with applied machine learning methods and natural language processing for the early detection of dementia. Nicklas is the author of numerous conference and journal publications, which have been awarded several Best Paper Awards. Today, Nicklas is the full-time manager of ki elements, which he founded in 2017 together with other DFKI scientists. The start-up pioneers the use of speech and language as clinical biomarkers for several neurological and psychiatric indications.
Industry use cases for AI in Medicine
Thu, July 22, 2021
|9:00-10:00 CET – Keynote 3 (joint with Track A)
Joanna Bryson (Hertie School)
Joanna Bryson is recognised for broad expertise on intelligence and its impacts, advising governments, transnational agencies, and NGOs globally. She holds two degrees each in psychology and AI (BA Chicago, MSc & MPhil Edinburgh, PhD MIT). From 2002-19 she was Computer Science faculty at Bath; she has also been affiliated with Harvard Psychology, Oxford Anthropology, Mannheim Social Science Research, and the Princeton Center for Information Technology Policy. During her PhD she observed confusion generated by anthropomorphised AI, leading to her first AI ethics publication “Just Another Artifact” in 1998. In 2010 she coauthored the first national-level AI ethics policy, the UK’s Principles of Robotics. She presently researches the impact of technology on human cooperation, and AI/ICT governance.
|10:00-13:00 CET – Course 5
Tim Dahmen (DFKI)
Tim Dahmen holds a PhD in computer science (Saarland University) and is senior researcher at the DFKI, where he leads the team Computational 3D Imaging. He conducts interdisciplinary research in the combination of computer graphics and artificial intelligence, mainly in the biomedical and microscopy domain. His current work focuses on using parametric generative models to generate synthetic training data for deep learning systems. He has contributed to the field of sparse and adaptive data acquisition in electron microscopy and to Deep Learning enabled mechanical simulation.
Bio-mechanical simulation for individualized implants
|14:30-17:00 CET – Course 6
Denis Engemann (Inria)
Denis Engemann holds a PhD in experimental psychology (University of Cologne) and is a research scientist at Inria Saclay, Parietal team. He conducts interdisciplinary research targeting brain and mental health by combining machine learning with large-scale analysis of brain signals. During his postdoc (ICM Paris & Neurospin), he studied consciousness-revealing EEG dynamics in severely brain injured patients. His current applied work focuses on detecting aging-related cognitive dysfunction from MEG and EEG. He has been contributing to the MNE software since 2012 and is actively working on methods and tools facilitating the analysis of clinical EEG data.
Building EEG-based proxy measures of brain aging in Python with MNE and its ecosystem
|17:30-19:00 CET – Participant posters and demos (joint with Track A)|
Fri, July 23, 2021
|9:00-10:00 CET – Keynote 4
Gerd Reis (DFKI)
Gerd Reis is a senior researcher and deputy director at DFKI which he joined in 2001. He received his PhD in computer science from the Technical University of Kaiserslautern on the topic of 4D Cardiac Ultrasound. Since 2008 he is lead reasearcher and developer of the ANNA/C-TRUS system, an medical product to aid urologists in the diagnosis of prostate cancer. He has also developed a continuous bladder irrigation system to prevent complications caused by blood clots after operations on the bladder, prostate, or kidneys.
AI in Medicine - An engineering perspective
|10:00-13:00 CET – Course 7
Pierre Zweigenbaum (CNRS - LIMSI)
Pierre Zweigenbaum is a Senior Researcher at LISN (Orsay, France), a laboratory of CNRS and Université Paris-Saclay, where he led the ILES Natural Language Processing team for seven years. Before CNRS he was a researcher at Paris Public Hospitals and a part-time professor at the National Institute for Oriental Languages and Civilizations. His research focus is Natural Language Processing, with medicine as a main application domain. He is the author or co-author of methods and tools to detect various types of medical entities, expand abbreviations, resolve co-references, detect relations, and link text to biomedical terminologies and ontologies. He graduated from École Polytechnique and Télécom Paris, holds a PhD in Computer Science from Télécom Paris and an habilitation in Computer Science from Université Paris-Nord.
Natural Language Processing for medical applications
|14:30-17:00 CET – Course 8
Philipp Klumpp, Tomás Arias-Vergara, and Paula Andrea Pérez-Toro (Friedrich-Alexander-University Erlangen-Nürnberg)
Philipp Klumpp is currently pursuing his Ph.D. degree at Pattern Recognition Lab of Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany. He received his B.Sc. and M.Sc. in Biomedical Engineering from FAU in 2014 and 2017, respectively. Working for the speech group, his research focus lies on the analysis of pathological speech signals of Parkinson’s Disease, Aphasia, Dysarthria and other neurological conditions. His general research interests lie in the fields of pattern recognition, pattern analysis and machine learning, particularly deep learning for sequential data analysis.Tomás Arias-Vergara received the B.S. degree in Electronics Engineering from University of Antioquia (Medellin, Colombia) in 2014, and the M.Sc. degree at the same institution in 2017. Currently, he is a doctoral candidate at the GITA Lab from the University of Antioquia (Colombia) and at Pattern Recognition Lab from the Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) in a joint degree program. Additionally, he is a guest researcher in the Department of Otorhinolaryngology, Head and Neck Surgery from the Ludwig-Maximilians-Universität München (Germany). His research interests include speech processing, signal processing, pattern recognition, machine learning and their applications in pathological speech.Paula Andrea Pérez-Toro received the B.S. degree in Electronics Engineering from University of Antioquia (Medellin, Colombia) in 2018 and the M.Sc. degree at the same institution in 2021. Currently, she is a PhD. candidate at Pattern recognition Lab from the Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) and at the GITA Lab from the University of Antioquia (Colombia). She has performed research activities related to signal processing, machine learning, and deep learning for health-care during the last three years, both in academic and industrial partners. Her research interests include speech processing, signal processing, pattern recognition, natural language processing, gait analysis, and their applications in health care and customer service.
Automatic analysis of pathologic speech – from diagnosis to therapy
|17:30-19:00 CET – Collaborative wrap-up (joint with Track A)|