SSIMA Re:Imagine Healthcare
Festival of Innovation in MedTech
SSIMA is the annual international MedTech festival in Eastern Europe, covering topics related to medical technology, such as: Deep Learning and Big Data in Healthcare, Medical Imaging, Medical Sensors and Robots and AI-based Data Analysis.
It covers these topics from academic, business and public policy perspectives, with the explicit aim of increasing relationships between Eastern European academics, companies and governments and their global counterparts. As the only MedTech event in Eastern Europe endorsed by MICCAI society, we feel privileged to have a wide audience, crossing international borders, in our pursuit of these purposes.
SSIMA’s speakers in past years include professors from MIT, Johns Hopkins, Harvard University, French Academy of Sciences, Technion Israel Institute of Technology, Eindhoven University and many others. National level policy makers and representatives from multinational companies have also participated. SSIMA is the place to learn, connect and decide on all MedTech matters concerning you or your organization.
Prof. Alejandro Frangi, PhDUniversity of Leeds, UK
Prof. Wiro Niessen, PhDErasmus University MC, NL | CEO Quantib
Prof. Nikos ParagiosEcole CentraleSupelec, FR | CEO TheraPanacea
Prof. Bart ter Haar Romeny, PhDEindhoven University of Technology, NL
Prof. Marius Leordeanu, PhDPolitehnica University Bucharest, RO | Institute of Mathematics Simion Stoilow, Romanian Academy
Prof. Mehmet Erturk, PhDSBU Mehmet Akif Ersoy Chest Cardiovascular Surgery Training and Research Hospital, TR
Stefan Carp, PhDHarvard Medical School, US
Prof. Ovidiu Andronesi, PhD, MDHarvard Medical School, US
Prof. Peter Meer, PhDRutgers University, US
Prof. Nahum Kiryati, PhDTel Aviv University, IL
Prof. Alon Wolf, PhDTechnion-Israel Institute of Technology, IL
Prof. George Verghese, PhDMassachusetts Institute of Technology, US
M. Alex O. Vasilescu, PhDAssoc.Director, UCLA Computer Vision and Graphics Lab | CSO, Tensor Vision
Alejandro (Alex) was born in La Plata, Argentina. In 1991 he moved to Barcelona, Spain, where he obtained his undergraduate degree in Telecommunications Engineering from the Technical University of Catalonia (Barcelona) in 1996. Then he carried out research on electrical impedance tomography for image reconstruction and noise characterization at the same institution under a CIRIT grant. In 1997 he obtained a grant from the Dutch Ministry of Economic Affairs to pursue his PhD in Medicine at the Image Sciences Institute of the University Medical Center Utrecht on model-based cardiovascular image analysis. During this period he was visiting researcher at the Imperial College in London, UK, and in Philips Medical Systems BV, The Netherlands.
Professor Frangi is Diamond Jubilee Chair in Computational Medicine at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He leads the CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine. He has been awarded a Royal Academy of Engineering Chair in Emerging Technologies (2019-2029).
Professor Frangi has edited several books, published 7 editorial articles and over 215 journal papers in key international journals of his research field and more than over 200 book chapters and international conference papers with an h-index 55 and over 20,700 citations according to Google Scholar. He has been three times Guest Editor of special issues of IEEE Trans Med Imaging, one on IEEE Trans Biomed Eng, and one of Medical Image Analysis journal. He was chair of the 3rd International Conference on Functional Imaging and Modelling of the Heart (FIMH05) held in Barcelona in June 2005, Publications Chair of the IEEE International Symposium in Biomedical Imaging (ISBI 2006), Programme Committee Member of various editions of the Intl Conf on Medical Image Computing and Computer Assisted Interventions (MICCAI) (Brisbane, AU, 2007; Beijing CN, 2010; Toronto CA 2011; Nice FR 2012; Nagoya JP 2013), International Liaison of ISBI 2009, Tutorials Co-Chair of MICCAI 2010, and Program Co-chair of MICCAI 2015. He was also General Chair for ISBI 2012 held in Barcelona. He is the General Chair of MICCAI 2018 held in Granada, Spain.
Professor Frangi is Chair of the Editorial Board of the MICCAI-Elsevier Book Series (2017-2020), and serves as Associate Editor of IEEE Trans on Medical Imaging, Medical Image Analysis, SIAM Journal Imaging Sciences, Computer Vision and Image Understanding journals. Professor Frangi was foreign member of the Review College of the Engineering and Physical Sciences Research Council (EPSRC, 2006-10) in UK, is a recipient of the IEEE Engineering in Medicine and Biology Early Career Award in 2006, the ICT Knowledge Transfer Prize (2008) and two Teaching Excellence Prizes (2008, 2010) by the Social Council of the Universitat Pompeu Fabra. He also was awarded the UPF Medal (2011) for his service as Dean of the Escuela Politècnica Superior. He was awarded the ICREA-Academia Prize by the Institució Catalana de Recerca i Estudis Avançats (ICREA) in 2008. Professor Frangi is an IEEE Fellow (2014), EAMBES Fellow (2015), SPIE Member, SIAM Member, MICCAI Member, and elected member to the Board of Directors of the Medical Image Computing and Computer Assisted Interventions (MICCAI) Society (2014-2018). Professor Frangi serves in the Scientific Advisory Board of the European Institute for Biomedical Imaging Research (EIBIR) and was Chair of the Fellows Committee of the IEEE EMBS (2017-2018).
Responsibilities & affiliations
Member of LIFD Leeds Institute for Fluid Dynamics
Professor Frangi’s main research interests lie at the crossroad of medical image analysis and modeling with emphasis on machine learning (phenomenological models) and computational physiology (mechanistic models). He has particular interest in statistical methods applied to population imaging and in silico clinical trials. His highly interdisciplinary work has been translated to the areas of cardiovascular, musculoskeletal and neuro sciences.
He been principal investigator or scientific coordinator of over 25 national and European projects, both funded by public and private bodies. During 1/2006-3/2010 he was coordinator of the @neurIST: Intergrated Biomedical Informatics for the Management of Cerebral Aneurysms, a 12.6M€ European Integrated Project, during 1/2006-12/2009 he was scientific co-PI for the Spanish CENIT Technology Platform CDTEAM funded with 15.7M€ by the Spanish Ministry of Science and Innovation through CDTI, in 2009-2012 he participated of the euHeart Integrated Project, in 2009-2012 in the Virtual Physiological Human Network of Excellence, and in 2009-2012 he was Scientific Coordinator of the CENIT Technology Platform cvREMOD funded with 13.6M€ by the Spanish Ministry of Science and Innovation through CDTI. He coordinates the € 13.3m-Integrated Project funded by the European Commission entitled VPH-DARE@IT DementiA Research Enabled by IT, led by CISTIB and involving other 19 European organizations. Finally, he has been recently awarded a £ 1.3m grant as Principal Investigator from the UK Engineering and Physical Sciences Research Council (EPSRC) for the project OCEAN: One-stop-shop Microstructure-sensitive Perfusion/Diffusion MRI: Application to Vascular Cognitive Impairment He is also one of the 5 co-investigators in the recently awarded EPSRC-NIHR HTC Partnership Award ‘Plus’: Medical Image Analysis Network (MedIAN) led by Oxford.
Under his leadership, CISTIB develops GIMIAS (Graphical Interface for Medical Image Analysis and Simulation, an open-source platform for rapidly developing pre-commercial software prototypes in the areas of image computing and image-based computational physiology modelling, and MULTI-X (Health Data Analytics and Modelling As a Service Platform), a cloud-based platform for computational phenomics, in silico medicine, and in silico clinical trials. The research and development conducted in his research group led to two spin-off companies Clintelis SA in 2009 and GalgoMedical SA (www.galgomedical.com) in 2013.
BSc/MSc Telecommunications Engineering, Universitat Politecnica de Catalunya, 1996, Barcelona, Spain
PhD Imaging Sciences, Utrecht University, 2001, Utrecht, The Netherlands
Prof. Wiro Niessen, PhD
Wiro Niessen was born in Geldrop, The Netherlands, on November 15, 1969. He received a MSc degree (cum laude) in Physics from Utrecht University, the Netherlands in 1993, and a PhD degree (cum laude) in Medical Imaging from Utrecht University, the Netherlands, in 1997. Part of the MSc programme was carried out at the University of Wisconsin, Madison, USA, and part of the PhD programme at Yale University, USA.
From 1997-2004 he was Postdoctoral Researcher, Assistant Professor and Associate Professor at the Image Science Institute of the University Medical Center Utrecht. In this period he was leading research theme groups in the fields of cardiovascular image analysis and image guided interventions, and supervised eleven PhD theses in these fields.
In 2005, he was appointed as a Full Professor of Biomedical Image Processing in the Departments of Radiology and Medical Informatics at the Erasmus MC. His research interests include many aspects of computer vision, biomedical image analysis, and computer assisted interventions. In 2005 he was also appointed Professor at Delft University of Technology, at the faculty of Applied Sciences.
Wiro Niessen was elected to the Dutch Young Academy (DJA) in 2005, and is Board Member of the Dutch Young Academy since 2009. He is Associate Editor of the IEEE Transactions on Medical Imaging and Elsevier’s Medical Image Analysis. He served as a Guest Editor on two Special Issues on Model-Based Image Analysis, and Growth and Motion Analysis of the IEEE Transactions on Medical Imaging, on a Special Issue on Medical Image Computing and Computer Assisted Intervention of Medical Image Analysis, and on a special issue on mathematical methods in biomedical image analysis in the International Journal of Computer Vision. He has (co)-authored over 230 publications, abstracts excluded, in the fields of computer vision, medical image analysis and image guided surgery, of which over 90 are in peer-reviewed international journals, and of which 5 received best paper awards.
Distinguished Professor of Mathematics
Founder, CEO and CTO of TheraPanacea
Nikos Paragios (D.Sc. (05), PhD (00), M.Sc. (96), B.Sc. (94)) is professor of Applied Mathematics and Computer Science, director of the Center for Visual Computing of Ecole Centrale de Paris & Ecole des Ponts – ParisTech, and scientific leader of GALEN group of Ecole Centrale de Paris/INRIA Saclay, Ile-de-France.
Professor Paragios is the founder, CEO and CTO of TheraPannacea, a company that uses artificial intelligence to improve traditional radiation therapy.
Prior to that he was professor of applied mathematics at Ecole Centrale de Paris (2005-2011), professor/research scientist (2004-2005) at the Ecole Nationale de Ponts et Chaussees, affiliated with Siemens Corporate Research (Princeton, NJ, 1999-2004) as a project manager, senior research scientist and research scientist. Professor Paragios was an adjunct professor at Rutgers University (2002 ) and at New York University (2004) and visiting professor at Yale (2007) and at University of Houston (2009).
Professor Paragios is an IEEE Fellow, has co-edited three books, published more than two hundred papers in the most prestigious journals and conferences of medical imaging and computer vision (DBLP server), and holds twenty one US patents. Professor Paragios is the Editor in Chief of the Computer Vision and Image Understanding Journal and has served/serves as an associate/area editor/member of the editorial board for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), the Computer Vision and Image Understanding Journal (CVIU), the International Journal of Computer Vision (IJCV), the Medical Image Analysis Journal (MedIA), the Journal of Mathematical Imaging and Vision (JMIV), the Imaging and Vision Computing Journal (IVC), the Machine Vision and Applications (MVA) Journal and the SIAM Journal in Imaging Sciences (SIIMS) while he was one of the program chairs of the 11th European Conference in Computer Vision (ECCV’10, Heraklion, Crete) and serves regularly at the conference boards of the most prestigious events of his fields (ICCV, CVPR, ECCV, MICCAI).
Professor Emeritus of Biomedical Image Analysis
Department of Biomedical Engineering
Eindhoven University of Technology
Bart M. ter Haar Romeny is professor emeritus of Biomedical Image Analysis at the Department of Biomedical Engineering of Eindhoven University of Technology. His interests are medical image analysis, its foundations and clinical applications. In his research, in order to understand image structure and analysis, he takes a closer look at the human visual system. His interests are in particular the mathematical modelling of human visual perception to build optimal computer-aided diagnosis systems, multi-scale and multi-orientation differential geometry, medical visualization applications, and brain connectivity. He authored about 350 papers, conference contributions and book chapters on these issues, and holds 2 patents. His interactive tutorial book on perceptually inspired multi-scale image analysis and his edited book on non-linear diffusion theory in computer vision are widely used. He is project leader of the Sino-Dutch RetinaCheck project: AI supported screening for early signs of eye damage due to diabetes.
Bart M. ter Haar Romeny studied Applied Physics at Delft University of Technology where he received his MSc in 1978. He subsequently performed his military service as a Royal Dutch Navy officer and obtained his PhD from Utrecht University in 1983. He then became the principal physicist of the Utrecht University Hospital Radiology Department and (1986-1989) clinical project leader of the Dutch project in Picture Archiving and Communication Systems. From 1989-2001, he was Associate Professor at the Image Sciences Institute (ISI) of Utrecht University. In 2001 he was appointed full processor of Biomedical Image Analysis at the Department of Biomedical Engineering of Eindhoven University of Technology, where he retired in 2017. In 2010, Bart ter Haar Romeny was appointed as Distinguished NEU 100-Program Professor in Biomedical Image Analysis at Northeastern University (Shenyang, PR China). Since 2014, he is a Visiting Professor at the Chinese Academy of Science in Beijing (PR China) and since 2017, he is a Honorary Chair Professor in Medical Image Analysis at the National Taiwan University of Science & Technology in Taipei (Taiwan).
Prof. Marius Leordeanu, PhD
Prof. Mehmet Erturk, PhD
Chief Physician associate professor dr. Mehmet Erturk was born in 1975 in Trabzon, Turkey. He completed his primary and secondary education in Trabzon. He started his university life at 19 May Medical Faculty in 1995 and graduated from Istanbul University Istanbul Medical Faculty in 2001. He completed his Cardiology Specialist training at SBU Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital between 2001-2006. He worked as a cardiologist at Istanbul Yavuz Sultan Selim State Hospital between 2006-2009. In March 2009, he started to work as a specialist in the Cardiology Department. In 2010, he became the chief assistant at the Cardiology Clinic.
In 2014, he spent 3 months at Emony University (Atlanta, USA) as an Observer. In the same year, he became an associate professor. In 2016, for a period of 2 months, he worked at Sadi Konuk Training and Research Hospital and Şişli Etfal Training and Research Hospital, Cardiology Clinic.
At the end of 2016, he was appointed to SBU Mehmet Akif Ersoy Chest Cardiovascular Surgery Training and Research Hospital as proxy. In February 2017, he was appointed to SBU Mehmet Akif Ersoy Chest Cardiovascular Surgery Training and Research Hospital as a manager.
Since December 2017, our hospital has been working as Chief Physician.
He is married and has three children.
Stefan Carp, PhD
Assistant Professor, Radiology, Harvard Medical School
Assistant in Biomedical Engineering, Radiology
Martinos Center, Massachusetts General Hospital
Building 149, Room 2301, 13th Street, Charlestown, MA 02129 USA
Email: firstname.lastname@example.org , tel. +1-617-643-2230
- D. Chemical Engineering, U.C. Irvine, 2005
- S. Chemical Engineering, MIT, 2000
- S. Chemistry, MIT, 2000
My research focuses on the development of novel optical instrumentation for non-invasive tissue monitoring and the translation of these techniques for clinical use. In particular I am interested in the application of near-infrared diffuse optical tomography techniques for breast cancer diagnosis and chemotherapy guidance. We have recently shown that dynamic changes in tissue blood volume during fractional mammographic-like compression can be used to assess the early response of malignant lesions to neoadjuvant chemotherapy. I am currently preparing to conduct a follow-up study, that uses a multi-modal optical imaging system combined with digital breast tomosynthesis to characterize the outcome early prediction performance of dynamic optical biomarkers assessed during a multi-stage breast compression maneuver for HER2+ and TNBC patients undergoing NACT.
Assistant Professor in Radiology
Center for Biomedical Imaging
Massachusetts General Hospital
Harvard Medical School
Prof. Peter Meer, PhD
Peter Meer received the Dipl. Engn. degree from the Bucharest Polytechnic Institute, Romania in 1971, and the D.Sc. degree from the Technion, Israel Institute of Technology, Haifa, in 1986, both in electrical engineering. From 1971 to 1979 he was with the Computer Research Institute, Cluj, Romania, working on R&D of digital hardware. Between 1986 and 1990 he was Assistant Research Scientist at the Center for Automation Research, University of Maryland at College Park. In 1991 he joined the Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ and retired in 2018 as Distinguished Professor. He has held visiting appointments in Japan, Korea, Sweden, Israel and France, and was on the organizing committees of numerous international workshops and conferences. He was an Associate Editor of the IEEE Transaction on Pattern Analysis and Machine Intelligence between 1998 and 2002, was a Guest Editor of Computer Vision and Image Understanding for a special issue on robustness in computer vision in 2000, and was a member of the Editorial Board of Pattern Recognition between 1989 and 2005. He is coauthor of an award winning paper in Pattern Recognition in 1989, the best student paper in 1999, the best paper in 2000 and the runner-up paper in 2007 in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). With coautors Dorin Comaniciu and Visvanathan Ramesh he received at the 2010 CVPR the Longuet-Higgins prize for fundamental contributions in computer vision in the past ten years. His research interest is in application of modern statistical methods to image understanding problems. He is an IEEE Life Fellow.
Prof. Nahum Kiryati, PhD
Nahum Kiryati is a professor in the School of Electrical Engineering at Tel Aviv University. He teaches and carries out research in the field of Image Processing and Computer Vision, and founded the Vision and Image Analysis (VIA) laboratory.
Prof. Kiryati obtained the B.Sc. degree in Electrical Engineering, summa cum laude, from Tel Aviv University in 1980. He received the M.Sc. and D.Sc. degrees from the Technion, Haifa, in 1988 and 1991 respectively. He was a visiting assistant professor at the Swiss Federal Institute of Technology (ETH Zurich) and a faculty member at the Technion. Since 1998 he is with Tel Aviv University.
Prof. Kiryati authored numerous articles in Image Processing, Video Analysis, Computer Vision, Medical Imaging and related fields. Having advised many graduate students, a few of his former students are now faculty members in leading universities.
Over the years, Prof. Kiryati’s research has been supported by various external funding sources, including the German-Israeli Foundation (GIF), the European Union, the Chief Scientist of the Ministry of Trade and Industry and others. Prof. Kiryati has served on dozens of conference committees, as well as in editorial positions in professional journals.
In addition to his engineering activities, Prof. Kiryati obtained academic degrees in the Humanities and in Law. His legal interests include models for the funding of innovation, and the interplay between law and technology.
Prof. Kiryati is a Senior Member of the IEEE, and a member of the Israeli Bar Association.
Prof. Alon Wolf, PhD
Director and founder Biorobotics and Biomechanics LAB (BRML)
Mechanical Engineering Department
Technion-Israel Institute of Technology
Vice President of Technion-Israel Institute of Technology
Co- Inventor and founder Merdrobotics Corporation
Alon Wolf earned all of his academic degrees from the faculty of Mechanical Engineering at the Technion Israel Institute of Technology in Israel, receiving his Ph.D. in 2002. Immediately after receiving his Ph.D., he joined the Mechanical Engineering department at Carnegie Mellon University (CMU) as a Post-Doctoral Research Associate.
In March of 2006 Prof. Alon Wolf joined the faculty of Mechanical Engineering at the Technion Israel Institute of Technology. Here he founded a new research lab, the Biorobotics and Biomechanics Lab (BRML). The objective of the research in the BRML is to develop fundamental theories in bio-kinematics and biomechanics as well as to apply these theories to applications in medical robotics and biorobotics. Prof. Wolf’s work was published in many leading journals, Book chapters, and conferences.
He is a co-inventor and co-founder of Medrobotics and is an Associate Editor for the prestige journal of Clinical Biomechanics. He won numerous research awards and was elected to the 2016-2017 IEEE Engineering in Medicine & Biology Society Distinguished Lecturer Program.
Prof. Wolf’s urban search and rescue snake robot and his surgical snake robot were elected best technology of 2012 and 2014 respectively, by the prestige journal of popular science.
Professor of Professor of Electrical and Biomedical Engineering
Department of Electrical Engineering and Computer Science
MIT – Massachusetts Institute of Technology
George C. Verghese received his BTech from the Indian Institute of Technology, Madras in 1974, his MS from the State University of New York, Stony Brook in 1975, and his PhD from Stanford University in 1979, all in Electrical Engineering. Since 1979, he has been with MIT, where he is the Henry Ellis Warren (1894) Professor, and Professor of Electrical and Biomedical Engineering, in the Department of Electrical Engineering and Computer Science. He was named a MacVicar Faculty Fellow at MIT for the period 2011-2012, for outstanding contributions to undergraduate education.
Verghese is also a principal investigator with MIT’s Research Laboratory of Electronics (RLE). His research interests and publications are in the areas of dynamic systems, modeling, estimation, signal processing, and control. Over the past decade, his research focus has shifted from applications in power systems and power electronics entirely to applications in biomedicine. He directs the Computational Physiology and Clinical Inference Group in RLE.
M. Alex O. Vasilescu, PhD
Multilinear tensor modeling methods are particularly well suited for mathematically representing cause-and-effect of multi-modal data where an observation, such as an image, is the result of several causal factors of data formation.
For example, natural images are the compositional consequence of multiple factors related to scene structure, illumination, and imaging. The appearance of a person in an image (ie. pixel values) is the result of the facial geometry of a person, camera location/parameters, lighting conditions, expression, etc. While we can directly observe and measure the gray (or color) values in an image, we are often more interested in the information associated with the causal factors that determine the pixel values in an image, such as the person’s identity, the viewing direction, or expression, which may be inferred, but not directly measured. The causal factors are represented by the latent variables in a computational model.
Data tensor modeling was first employed in computer vision, computer graphics and machine learning to representing cause-and-effect and demonstratively disentangle the causal factors of observable data and recognize people from the way they move (Human Motion Signatures in 2001) and from their facial images (TensorFaces in 2002), but it may be used to recognize any objects or object attributes.
There are two classes of data tensor modeling techniques: (1) rank-K tensor decompositions (CANDECOMP / Parafac decomposition) and (2) rank-(R1,R2,…,RM) tensor decompositions, (Tucker decomposition). There are variants on these decompositions which employ various constraints, as well as kernel variations which apply a pre-processing step.
Recent theoretical evidence shows that deep learning is a neural network equivalent to multilinear tensor decomposition, while a shallow network corresponds to CP tensor factorization (aka, linear tensor factorization).
TensorFaces is based on the insight that multilinear tensor methods can explicitly model and decompose a facial image in terms of the causal factors of data formation where each causal factor is represented according to their second-order statistics by employing the Tucker tensor decomposition. We refer to this approach more generally as Mulitlinear PCA in order to better differentiate it from our Multilinear ICA approach.
Multilinear (tensor) ICA is a more sophisticated model of cause-and-effect based on the higher-order statistics associated with each causal factor. Similarly, one can employ our kernel variants (pg.43 ) to model cause-and-effect. By comparison, matrix decompositions, such as PCA, or ICA, capture the overall statistical information (variance, kurtosis) without any type of differentiation.
Subspace multilinear learning demonstratively disentangles the causal factors of data formation through strategic dimensionality reduction. For example, in the case of facial images (or bi-directional textures functions), we suppress illumination effects such as shadows and highlights without blurring the edges associated with the person’s identity that are important fo recognition (or edges associated with structural information that are important for texture synthesis. See TensorTextures video below. ).
Next important question: While TensorFaces is a handy moniker for an approach that learns and represents the interaction of various causal factors from a set of training images, with Multilinear (Tensor) ICA as a more sophisticated approach that employs higher order statics, neither interaction model prescribes a solution for how one might determine the causal factors of a single unlabeled test image.
Multilinear Projection (FG 2011 , ICCV 2007 ) addresses the recognition question: How does one determine from a single unlabeled test image all the unknown causal factors of data formation, ie how does one solve for multiple unknowns from a single image equation? In the course of addressing this question, several concepts from linear (matrix) algebra were generalized, such as the mode-m identity tensor (which is also an algebraic operator that reshapes a matrix into a tensor and back again to a matrix), the mode-m pseudo-inverse tensor, the mode-m product in order to develop the multilinear projection algorithm. (Note: The mode-m pseudo-inverse tensor is not a tensor pseudo-inverse.) Multilinear projection simultaneously projects one or more unlabeled test images into multiple constituent mode spaces, associated with image formation, in order to infer the mode labels.
“Compositional Hierarchical Tensor Factorization for Appearance-Based Recognition”, M.A.O. Vasilescu, E. Kim, 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2019) Workshop on Tensor Methods for Emerging Data Science Challenges, August 5, 2019.
“Face Tracking with Multilinear (Tensor) Active Appearance Models”, Weiguang Si, Kota Yamaguchi, M. A. O. Vasilescu , June, 2013.Paper (pdf)
“Multilinear Projection for Face Recognition via Canonical Decomposition “, M.A.O. Vasilescu, In Proc. Face and Gesture Conf. (FG’11), 476-483.Paper (pdf)
“Multilinear Projection for Face Recognition via Rank-1 Analysis “, M.A.O. Vasilescu, CVPR, IEEE Computer Society and IEEE Biometrics Council Workshop on Biometrics, June 18, 2010.
“Multilinear Projection for Appearance-Based Recognition in the Tensor Framework”, M.A.O. Vasilescu and D. Terzopoulos, Proc. Eleventh IEEE International Conf. on Computer Vision (ICCV’07), Rio de Janeiro, Brazil, October, 2007, 1-8. Paper (1,027 KB – .pdf)
“Multilinear Independent Components Analysis and Multilinear Projection Operator for Face Recognition”, M.A.O. Vasilescu, D. Terzopoulos, in Workshop on Tensor Decompositions and Applications, CIRM, Luminy, Marseille, France, August 2005.
“Multilinear (Tensor) ICA and Dimensionality Reduction”, M.A.O. Vasilescu, D. Terzopoulos, Proc. 7th International Conference on Independent Component Analysis and Signal Separation (ICA07), London, UK, September, 2007. In Lecture Notes in Computer Science, 4666, Springer-Verlag, New York, 2007, 818–826.
“Multilinear Independent Components Analysis”, M. A. O. Vasilescu and D. Terzopoulos, Proc. Computer Vision and Pattern Recognition Conf. (CVPR ’05), San Diego, CA, June 2005, vol.1, 547-553. Paper (1,027 KB – .pdf)
“Multilinear Independent Component Analysis”, M. A. O. Vasilescu and D. Terzopoulos, Learning 2004 Snowbird, UT, April, 2004.
“Multilinear Subspace Analysis for Image Ensembles,” M. A. O. Vasilescu, D. Terzopoulos, Proc. Computer Vision and Pattern Recognition Conf. (CVPR ’03), Vol.2, Madison, WI, June, 2003, 93-99. Paper (1,657KB – .pdf)
“Multilinear Image Analysis for Facial Recognition,” M. A. O. Vasilescu, D. Terzopoulos, Proceedings of International Conference on Pattern Recognition (ICPR 2002), Vol. 2, Quebec City, Canada, Aug, 2002, 511-514. Paper (439KB – .pdf)
“Multilinear Analysis of Image Ensembles: TensorFaces,” M. A. O. Vasilescu, D. Terzopoulos, Proc. 7th European Conference on Computer Vision (ECCV’02), Copenhagen, Denmark, May, 2002, in Computer Vision — ECCV 2002, Lecture Notes in Computer Science, Vol. 2350, A. Heyden et al. (Eds.), Springer-Verlag, Berlin, 2002, 447-460. Full Article in PDF (882KB)
|Morning||Overview of AI applications in everything today|
|The role of the biomedical engineer|
|Afternoon||How does AI work?|
|Startups Presentation: Success Stories|
|Morning||MRI, K-space to diagnosis, reduction scan time|
|Afternoon||AI in population imaging|
|AI in cancer detection (breast, lung, brain)|
|Learning from the brain / RetinaCheck|
|Aftrenoon||Availability of data, curation|
|Legal issues: protection and security|
|How to get medical data for R&D?|
|Morning||MRI, neural imaging|
|Medical Imaging grand challenges, Kaggle|
|Aftternoon||Play with trained networks|
|Morning||Overview of AI applications in everything today|
|The role of the biomedical engineer|
|Afternoon||How does AI work?|
|Startups Presentation: Success Stories|
|Morning||understand the basic concepts and principles of AI in medicine and particularly in Radiology (defining concepts and differentiating terminology)|
|To describe the potential role of AI in radiology management, diagnostic, simulation and education|
|Morning||To understand what imaging biomarkers are and the concept of radiomics|
|Explain how AI can be applied to the extraction and analysis of imaging biomarkers and radiomics|
|To provide an overview of the professional and legal implications of using AI (including medico-legal responsibility)|
|To address ethical problems and data protection issues (GDPR)|
|To explain how AI can be introduced in daily practice – from concept to validation and integration|
Conference venue at University Politehnica of Bucharest
SSIMA 2019 is to be hosted in the UPB’s Library/Conference Center (“Biblioteca Centrală UPB”). Please note that UPB’s campus is very vast, if to search for any information, use as reference the name of the university and the library building to locate the venue.
Here is a map of the UPB campus (click on the link for Google Maps):