Prof. José Manuel Fonseca
Faculty of Sciences and Technology of the Universidade Nova de Lisboa, Portugal
José Fonsecawas born in 1962, Lisbon, Portugal. Obtained his PhD on Electrical Engineering specialty Robotics and Integrated Manufacturing by Universidade Nova de Lisboa, 10th May 2001.He his Professor on the Faculty of Sciences and Technology of the Universidade Nova de Lisboa(FCT/UNL) since 1992, and member of the Electrical Engineering Department sinceits foundation on 1993. Has been responsible for several courses in the area ofsensorial processing and real-time processing. Since its PhD supervised two PhD. Thesis, 30 MSc Thesis and 35 Final Diploma Projects. Published more than 60 papers on Journals and International Conferences, is co-author of several book Chapters, co-author of a National Patent and co-founder of a spin-off Company. Prof. Fonseca has participated on more than ten European Projects as researcher and coordinator of the FCT/UNL participation. Was leader of task force e Delivery and leader of the work package Integrating Support Activities on the Network of Excellence “BIOPATTERN - Computational Intelligence for BioPattern Analysis to Support eHealth” FP6-2004-IST-2-508803 from 2004 to 2008.
Speech Title: "The (key) Role of Ergonomics in Digital Pathology"
Abstract: For more than a century that the optical microscope has been the pathologists primordial tool for detection/diagnosis of diseases such as cancer. Nowadays, digital pathology systems are not only the future but also the present. However, the huge size of whole-slide digital images, typically in the order of tens of gigapixel, makes it difficult for any image processing procedure and, therefore, any automatic analysis. Several studies highlight that the major challenges in this area are to achieve an image quality good enough and to provide an interactive visualization tool comparable to the experience of the optical microscope. Therefore, ergonomics and human-device interaction play a major role for the more generalized adoption of digital slides on daily clinical practice. Image viewing must be optimized to compete with the analogical viewing of traditional microscopes adopting a user interface carefully designed and using the most appropriated input devices to provide the friendliest and most effective environment with special emphasis on ergonomics. In this talk automatic segmentation algorithms for nucleoids identification on thyroid scraped slides will be addressed and IPATHSCOPE, a visualization tool developed with special emphasis on the ergonomics will be presented.
Prof. Tuan D. Pham
Linkoping University, Sweden
Tuan D. Pham is Professor of Biomedical Engineering at Linkoping University, University Hospital Campus, Linkoping, Sweden. Prior to the current position, he was appointed as Professor and Leader of the Aizu Research Cluster for Medical Engineering and Informatics, and the Medical Image Processing Lab, both at the University of Aizu, Japan. Before his appointments in Japan, he was the Bioinformatics Research Group Leader at the University of New South Wales, Canberra, Australia. He has been an Editorial Member and Associate Editor of Pattern Recognition (Elsevier), served as Guest Editor of Computer Methods and Programs in Biomedicine (Elsevier), Computers in Medicine and Biology (Elsevier), BioMedical Engineering OnLine (BioMed Central), and Associate Editor of IEEE Engineering in Medicine and Biology Conference series. Dr. Pham has published extensively on pattern recognition, image processing, and time-series analysis in medicine, biology, and mental health.
Speech Title: "Fuzzy Recurrence Analysis of Physiological Data"
Abstract: The concepts of fuzzy recurrence plots and scalable recurrence networks have recently been introduced. A fuzzy recurrence plot displays grayscale texture, which is informative for pattern analysis. Networks derived from fuzzy recurrence plots are scalable and can be naturally modeled as either unweighted or weighted. This talk presents several applications of fuzzy recurrence analysis of physiological data for pattern classification, including gait dynamics, computer keystroke time series, and photoplethysmography signals in control subjects and patients with neuro-degenerative diseases. Extension of fuzzy recurrence analysis to medical image data is also addressed in this talk.
Plenary Speaker I
Prof. Ralf Hofestädt
Bielefeld University, Germany
Prof. Ralf Hofestädt studied Computer Science and Bioinformatics at the University of Bonn. He finished his PhD 1990 (University Bonn) and his Habilitation (Applied Computer Science and Bioinformatics) 1995 at the University of Koblenz. From 1996 to 2001, he was Professor for Applied Computer Science at the University of Magdeburg. Since 2001, he is Professor for Bioinformatics and Medical Informatics at the University Bielefeld. The research topics of the department concentrate on biomedical data management, modeling and simulation of metabolic processes, parallel computing and multimedia implementation of virtual scenarios.
Speech Title: "OMPetri: A New Petri Net Simulation Environment based on OpenModelica"
Abstract: This talk will focus to a new Petri net simulation shell based on the OpenModelica software tool. A user interface will be presented, which allows the access to the Petri net library (PNlib) of OpenModelica. The PNlib-Shell provides a powerful simulation environment. Based on this new shell Petri net models can be easily created, simulated and analyzed. In addition the new system includes basic features to check and evaluate the model and to analyze simulation results generated by the simulation back-end.
Plenary Speaker II
Prof. Andre Ribeiro
Tampere University of Technology, Finland
Andre Ribeiro (andre.ribeiro AxT tut.fi) born in 1976, graduated in Physics in the University of Lisbon (1999) and has a PhD in Physics Engineering from IST, Technical University of Lisbon, Portugal (2004). From 2004-07, he was a Postdoc at the University of Calgary, Canada. Since 2008, he is the PI of the Laboratory of Biosystem Dynamics (LBD) at Tampere University of Technology (TUT), Finland. In April 2016 he created and is since then the head of the Multi-scaled Biodata Analysis and Modelling Research Community of TUT-UTA. Since June 2017, he is a Professor at the BiomediTech Institute, TUT. His studies focus on the in vivo dynamics and regulatory mechanisms of bacterial gene expression and genetic circuits at the single-cell, single-molecule level using time-lapse microscopy, stochastic models, molecular biosensors, single-cell signal processing, and synthetic gene engineering. The aims are to understand how genes and genetic circuits are regulated and unravel their range of functionalities, thereby assisting in the comprehensive engineering of synthetic circuits for regulating cellular processes. So far, he published 86 peer-reviewed journal articles, 41 peer-reviewed conference proceedings articles, 63 peer-reviewed conference abstract/posters, and 12 peer-reviewed book chapters. Also, he supervised 3 post-doctoral fellows, 12 PhD, 15 Masters, and 6 Bachelors theses. Finally, the LBD has 9 awards for scientific achievements and 2 awards for contributions to teaching.
Speech Title: "Bacterial Gene Regulatory Mechanisms of Decision-Making"
Abstract: Cells use past events in order to make decisions on future actions. This decision making process is based on the crossing of pre-established, tuned thresholds. At the level of gene expression and genetic circuits, these thresholds are based on RNA and protein numbers that need to be reached in order to change the status quo of the cell functioning. They are based on both maximum and minimum protein numbers, as well as on time-lengths that these numbers must be maintained so as to overcome safeguard mechanisms protecting the cell from spurious fluctuations in molecular numbers. We used in vivo single-RNA time-lapse fluorescence microscopy to extract the skewness and kurtosis of the distribution of intervals between consecutive RNA production events in individual cells. From the analysis of multiple promoters, mutants, induction schemes, and media conditions, we show that skewness and kurtosis can be tuned independently from the mean and noise in RNA numbers, by tuning the rate-limiting steps in transcription initiation. As these steps are sequence dependent and subject to regulation, these results suggest that, in bacteria, threshold crossing by RNA numbers and, thus, decision making by gene regulatory networks, is evolvable and adaptable at the stage of transcription initiation.
Plenary Speaker III
Assoc. Prof. David E. Breen
Drexel University, USA
David E. Breen is currently an Associate Professor of Computer Science in the College of Computing and Informatics of Drexel University. He has held research positions at the Max Planck Institute for the Physics of Complex Systems, the California Institute of Technology, the European Computer-Industry Research Centre, the Fraunhofer Institute for Computer Graphics, and the Rensselaer Design Research Center. His research interests include computer-aided design, biomedical image informatics, geometric modeling, self-organization and biological simulation. He has authored or co-authored over 100 technical papers, articles and book chapters on these and other subjects. He is the co-editor of the book "Cloth Modeling and Animation" and is a recipient of the prestigious NSF CAREER Award. Breen received a BA in Physics from Colgate University in 1982. He received MS and PhD degrees in Computer and Systems Engineering from Rensselaer Polytechnic Institute in 1985 and 1993.
Speech Title: "Volumetric Contour-Based Surface Reconstruction"
Abstract: Current imaging technology produces 3D sampled data that can be interpreted as a stack of 2D slices cutting through the studied object/specimen. Frequently the process of segmenting and identifying specific structures in the slices involves a delineation procedure that produces contours around the structure of interest in each slice. These contours then need to be connected in order to produce a 3D model of the structure. Given the noise properties and sampling resolutions of different imaging modalities, a single reconstruction technique is therefore unlikely to produce satisfactory results for all types of input. To address this issue, three volumetric contour-based surface reconstruction techniques have been developed. A volumetric/implicit approach has been taken because these types of techniques easily handle changes in topology and more robustly reconstruct complex multi-component, branching objects. The first two techniques are more suitable for sparse contour sets and are based on 2D level set morphing and 2D distance field interpolation. The third method is more appropriate for high density input that contains significant noise and utilizes an implicit point set model to create a smooth surface with user-specified error bounds. These three methods will be detailed and compared using numerous reconstruction results from a variety of contour datasets.
25 March 2018
Paper Acceptance Notification:
10 April, 2018
Camera-ready Paper Submission:
20 April, 2018
Conference Date: 16-17 May 2018
Academic Official Visit: 18 May 2018