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Keynote Lectures

Hayit Greenspan, Tel Aviv University, Israel
          Title: Deep Learning in Medical Applications

Marcus Cheetham, Universität Zürich, Switzerland
          Title: Virtual Humans - Challenges and Opportunities from a Psychological Perspective

Salvador Pané i Vidal, Swiss Federal Institute of Technology (ETH), Switzerland
          Title: Magnetically Guided Biomedical Micro- and Nanorobots

Luisa Torsi, University of Bari "A. Moro", Italy
          Title: Organic Bio-electronic Sensors for Ultra-sensitive Chiral Differential Detection



Deep Learning in Medical Applications

Hayit Greenspan
Tel Aviv University

Brief Bio
Prof. Greenspan heads the Medical Image Processing and Analysis Lab at the Biomedical Engineering Dept. in the Faculty of Engineering, Tel-Aviv University.  Prof. Greenspan has been conducting research in image processing and computer vision for the past 20 years, with a special focus on image modeling and analysis, resolution augmentation, and content-based image retrieval. Prof. Greenspan received the B.S. and M.S. degrees in Electrical Engineering from the Technion- Israel Institute of Technology, in 1986 and 1989, respectively, and the Ph.D. degree in Electrical Engineering from CALTECH – California Institute of Technology, in 1994. She was a Postdoc with the Computer Science Division at U.C. Berkeley from 1995 to 1997. In 1997 she joined Tel-Aviv University.
Prof. Greenspan has 35 journal publications in top-ranking journals, and more than 70 conference publications. She is the inventor of several patents. She is an Associate Editor of the top ranking journal in Medical imaging (IEEE-TMI) and is an active member of several international professional societies.

Machine learning is a key technology in medical image analysis. From unsupervised clustering of data to supervised learning of categories, statistical modeling tools are used across all modalities, from the molecular to MRI brain imagery. A leading trend in the machine learning community is Deep Learning which was termed one of the 10 breakthrough technologies of 2013 (MIT Technology Review, 2013). Within the Deep learning schemes, convolutional neural networks (CNNs) have become the most powerful technique for a range of different tasks in imaging and computer vision domains. CNNs are machine learning models that represent mid-level and high-level abstractions obtained from images.  An overview of the leading machine learning tools will be presented in the talk for various medical imaging tasks. I will also present the initial work emerging with Deep learning in this domain, reviewing the challenges facing this field and its initial promising results.  



Virtual Humans - Challenges and Opportunities from a Psychological Perspective

Marcus Cheetham
Universität Zürich

Brief Bio
Marcus Cheetham is Professor of Cognitive Psychology at Nungin University and Senior Research Associate at the Department of Neuropsychology, Zurich University. He received his Master's Degree in Psychology from Freiburg University, Germany, and his PhD in Neuropsychology from Zurich University. His research focuses on understanding the behavioural and neuropsychological mechanisms that underpin the experience of cognitive and emotional engagement with humanlike characters. This research uses various methodologies including eye-tracking, electroencephalography (EEG), structural and functional neuroimaging (MRI and fMRI) and traditional psychological research techniques.

Advances in computer-graphic technologies in realistically simulating aspects of human appearance, motion and (interactive) behaviour have lead to the question: How does engaging and interacting with ever more realistic virtual humans actually influence human subjective experience and behaviour? One general and seemingly counter-intuitive answer, formulated in the Uncanny Valley Hypothesis, is that relatively high levels of anthropomorphic realism are likely to evoke an unpleasant affective state characterised by feelings of strangeness and the uncanny. Consideration of the inconsistent findings in this new field of research suggests that this general answer is likely to be superseded by a more differentiated perspective that better reflects the psychological complexities of human nature. This perspective suggests that there will be great variability across individuals, stimuli, situations, tasks and over time in the relationship between anthropomorphic realism of virtual humans (or particular aspects of virtual humans) and human experience and behaviour. One of the many challenges that arises from this perspective lies in understanding how realistic a virtual human (or particular aspects of the virtual human) needs to be – in view of differences between individuals, stimuli, situations, tasks and time – to affect the outcomes for which a virtual human is designed. Psychology offers insight into this challenge and the opportunities that follow from this perspective.



Magnetically Guided Biomedical Micro- and Nanorobots

Salvador Pané i Vidal
Swiss Federal Institute of Technology (ETH)

Brief Bio
Dr. Salvador Pané i Vidal (Barcelona, 1980) is currently a Senior Research Scientist at the Institute of Robotics and Intelligent Systems (IRIS) at ETH Zürich.  He received a B.S. (2003), M.S (2004) and a PhD in Chemistry (2008) from the Universitat de Barcelona (UB) in the field of the electrodeposition of magnetic composites and magnetorresistive alloys.  He became a postdoctoral researcher at IRIS in August 2008 and Senior Research Scientist in 2012.  He has authored or co-authored 50 articles in international peer-reviewed journals and books for education in science.  Dr. Pané is currently working on bridging chemistry and electrochemistry with robotics at small scales.  In the field of micro- and nanororobotics, his major focus has been the miniaturization of magnetic materials and conductive polymers and hydrogels for targeted drug delivery.  He is the head of the IRIS electrochemistry laboratory at ETH, which he established in 2010.  At present, he teaches a course on nanorobotics and supervises eight on-going PhD theses. Dr. Pané is the coordinator for the MANAQA project (Magnetic Nanoactuators for Quantitative Analysis), which is funded by the EU commission under the Seventh Framework Programme (FP7/2007-2013) under Information and Communication Technologies (ICT). Dr. Pané was awarded the highly competitive Starting Grant from the European Research Council (ERC). The grant provides 1.5 million euros over five years to investigate composite nanomaterials with magnetoelectric properties for chemical and biomedical applications.

Over the past decade researchers have been developing micro- and nanorobots for use as biomedical platforms with applications such as chemical sensing and drug delivery. Understanding and controlling the physical and chemical interactions at the micro- and nanoscale is crucial for the realization of small biomedical robots. One of the main aspects investigated has been the fabrication and optimization of the motility component of these small agents, and one of the most promising approaches is to use electromagnetic systems to wirelessly control and actuate magnetic micro and nano structures. A goal of the research at the Multi-Scale Robotics Lab consists of creating untethered magnetically controlled micro and nanorobots to make current medical procedures safer and less invasive, and to create entirely new procedures that were never before possible. To increase their performance and to provide additional biofunctionalities (biocompatibility, drug delivery, sensing), other materials must be incorporated. In this work, we will present several magnetic micro- and nanoagents that have been produced in our laboratory with a focus on biomedical and environmental applications.



Organic Bio-electronic Sensors for Ultra-sensitive Chiral Differential Detection

Luisa Torsi
University of Bari "A. Moro"

Brief Bio
Luisa Torsi is full professor of Chemistry since 2005 and coordinator of the council for the degree courses in Materials Science and Technology at the University of Bari since 2010. She received her laurea degree in Physics from the University of Bari in 1989 and the PhD in Chemical Sciences from the same institution in 1993. She was post-doctoral fellow at Bell Labs from 1994 to 1996. In 2005 and 2006 she was invited professor at the University of Anger and Paris 7, respectively. In 2010 she has been awarded with the Heinrich Emanuel Merck prize for analytical sciences, this marking the first time the prestigious award is given to a woman. She is also the recipient of the 2013 “Best Italian Inventor Women” prize of the Italian Women Inventors & Innovators Network. Torsi is the vice-president of the European Material Research Society (E-MRS) and the elected president for 2016. She is the first women to hold both these roles in E-MRS. She is also member of the MRS and will serve as one of the Chairs of the MRS 2015 Fall Meeting (Boston).

The energies involved in weak chiral interactions occurring between odorant binding proteins (OBPs) and carvone enantiomers are evaluated, down to a few KJ/mol, by means of a water-gated organic field-effect transistor (WGOFET) whose Au-gate is modified with a porcine-OBP (pOBP) self-assembled monolayer. The output current measured is dependent on the concentration of the analyte and pM concentrations can be detected. The binding curves also are significantly different between the two enantiomers. The modelling of the two curves allows the energies associated with the OBP-carvone complexes formation to be independently extracted, from the very same set of data. From the dissociation constants the standard free-energy the complex formation at the electrode is derived, while the threshold voltage shifts gives information on the electrostatic component. This approach, representing a unique tool to quantitatively investigate low-energy bio-chemical interactions, is rather general as it relies on the relative dielectric constants of the protein-SAMs and of the organic semiconductors being much lower than that of water. The role of the OBPs in the olfaction system is still under debate and the detection of neutral odorant species at the pM level by means of a WGOFET adds relevant pieces of information to the understanding of the odor perception mechanism at the molecular level.