Prof. Kiyoshi Hoshino received two doctor's degrees; one in Medical Science in 1993, and the other in Engineering in 1996, from the University of Tokyo respectively. From 1993 to 1995, he was an assistant professor at Tokyo Medical and Dental University School of Medicine. From 1995 to 2002, he was an associate professor at University of the Ryukyus. From 2002, he was an associate professor at the Biological Cybernetics Lab of University of Tsukuba. He is now a professor. From 1998 to 2001, he was jointly appointed as a senior researcher of the PRESTO "Information and Human Activity" project of the Japan Science and Technology Agency (JST). From 2002 to 2005, he was a project leader of a SORST project of JST. He served as a member of the “cultivation of human resources in the information science field” WG, Special Coordination Funds for the Promotion of Science and Technology, MEXT, a member of “Committee for Comport 3D Fundamental Technology Promotion”, JEITA, and the chairman of the 43rd Annual Meeting of Japanese Society of Biofeedback Research.
Speech Title: "Estimation of the Line-of-Sight and Rotational Eye Movement by Tracking of Blood Vessel Images on the Eyeball Sclera"
Abstract: First, the measurement of the line-of-sight is expected to be effective in screening schizophrenia and dementia, as well as identifying patients with sick-house syndromes and drug addicts. Second, the biometry of the eye rotational movement, where the eyeball rotates around the z axis, is expected to be useful in detecting and quantifying visually-induced motion sickness, 3D sickness, car sickness, dizziness, discomfort, or sudden development of poor physical condition. And third, the gaze and the rotational eye movement influence each other, for instance, in the condition with the head or the body trunk tilted, as seen in playing sports, or where gravitational acceleration may affect the human visual system, as seen in car driving. This talk therefore focuses on a new method for estimating both the line-of-sight and the rotational eye movement day and night with a high degree of accuracy without imposing a psychological burden on a device-wearer, regardless of brightness of image contents. To meet these expectations, tracing the images of the characteristic template blood vessels is used to measure the user’s eye movements. The system can select the most appropriate template image with a characteristic shape according to brightness gradient direction of the edges on the eyeball sclera.
Prof. Yen-Wei Chen received his B.E. degree in 1985 from Kobe University, Kobe, Japan. He received his M.E. degree in 1987 and his D.E. degree in 1990, both from Osaka University, Osaka, Japan. From 1991 to 1994, he was a research fellow at the Institute of Laser Technology, Osaka, Japan. From October 1994 to March 2004, he was an associate Professor and a professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently a professor at the college of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan. He was a visiting professor at Oxford University, Oxford, UK in 2003 and at Pennsylvania State University, Pennsylvania, USA in 2010. He is also a chair professor at College of Computer Science and Technology, Zhejiang University, Hangzhou, China. He is associate Editors for the International Journal of Image and Graphics (IJIG), and the International Journal of Knowledge-based and Intelligent Engineering Systems. His research focuses on computer vision, medical image analysis and machine learning. He has published more than 300 research papers in these fields. He received various awards, such as Best Paper Award of ICPR2012, Paper Award of Meical Imaging Technology (Journal).
Speech Title: "Digital Atlas, Artificial Intelligence and Virtual Reality in Medical Applications"
Abstract: Atlas of human anatomy is an important teaching tool in the medical community. In the recent years, digital atlases of human anatomy have become popular and hot topics in medical image analysis research field. The basic idea of the digital atlas is to capture the organ variability of its position, shape and voxel intensity (texture) from a training set (either different individuals (inter-patient variability) or the same individual (intra-patient variability)). On the other hand, artificial intelligence (AI) and virtual reality (VR) play important roles in medicine and healthcare. In our Lab (Intelligent Image Processing Lab), we constructed computational abdominal atlas and developed advanced computer-aided detection/diagnosis (CAD) and surgery support systems by combining the atlases with AI and VR. In this keynote, I will talk about current progress and futures of computational anatomy, AI and VR in medical applications.
Prof. Qingli Li received the B.S. and M.S. degrees in computer science and engineering from Shandong University, Jinan, China, in 2000 and 2003, respectively, and the Ph.D. degree in pattern recognition and intelligent system from Shanghai Jiaotong University, Shanghai, China, in 2006. From 2012 to 2013, he was a visiting scholar at Medical Center, Columbia University, New York, USA. He is currently with the Key Laboratory of Polor Materials and Devices, East China Normal University, Shanghai, China. He is the author or coauthor of more than 50 papers published in various international journals and conference proceedings and a Principle Investigator (PI) for the National Natural Science Foundation of China (NSFC) projects. His research interests in biomedical engineering include molecular imaging, biomedical optics, and pattern recognition.
Speech Title: "Spectral Imaging and its Biomedical Applications"
Abstract: Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This presentation introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide an insight into the current technological advances and its potential for biomedical research.
Dr. Kuo-Yuan Hwa is an associate professor and the director of the Center for Biomedical Industries at the National Taipei University of Technology. Dr. Hwagraduated and received her PhD from the School of Medicine, the Johns Hopkins University. She is the president of the Medical Association for Indigenous Peoples of Taiwan (MAIPT). Dr. Hwa’s scientific interests are: 1) nanotechnology and biosensor, 2) new drug discovery for human diseases by proteomics and genomics approaches and 3) glycobiology, especially on enzymes kinetics. She has published 85 conference and journal articles and 10 patents. She has served in many national and international committees. Dr. Hwa has been invited as a speaker for many academic research institutes and universities in China, Korea, Japan and USA. She has been invited as a reviewer, a judge and an editor for international meetings and journals. In addition, one of her currently works is on developing culturally inclusive health science educational program, with both indigenous and western science knowledge for indigenous children.
Speech Title: "How to Build an in silico Platform from Bench to Bed Side in the Era of Precision Medicine"
Abstract: Precision medicine is a medical service model based on individual customization data such as genomics information, cellular molecular data and health record including disease records. Amongst all the human health care, precision medicine is most applicable to cancer prevention and treatment. Cancer is one of the dreadful diseases taking many lives worldwide. It is now clear that cancer is caused by a series of DNA mutations. Many cancer-related genes such as proto-oncogenes have been identified and mapped. Moreover, the formation of cancer is due to genomic mutations, often single nucleotide variant. Spontaneous or environmentally induced mutation occurs in a single cell, which then undergoes multiple cell divisions to form a tumour. Hence establishing cancer-related DNA variants database are important. Although there are many cancer-related DNA database, in this paper we have designed a workflow for establishing a precision medicine database system which consists of up breasted information of genes and DNA variants responsible for causing cancer. We have collected data from different databases and applied computational approach for the elicitation of beneficial outcomes from the large data sets. It will provide assistance to many researchers and clinicians in identifying different DNA variants linked with cancer and can provide the possible personalized healthcare treatment for various cancers.
Prof. Jose Nacher received his Ph.D. in Theoretical Physics from Valencia University. From 2003-2007 he was a postdoctoral research fellow at the Bioinformatics Center, Institute for Chemical Research (ICR), Kyoto University. He was awarded with a JSPS Research Fellowship at the ICR, Kyoto University (2005-2007). From 2007-2012, he was a Lecturer and an Associate Professor at the Department of Complex and Intelligent Systems, Future University, concurrently with a visiting Associate Professor appointment at the Bioinformatics Center, ICR, Kyoto University (2011-2102) and Future University (2012-2013), respectively. From 2012, he was an Associate Professor at the Department of Information Science, Toho University. Since 2016, he is a Professor at the Department of Information Science, Faculty of Science, Toho University. He is a reviewer of more than 30 international journals in his field, serves as an Editorial Review Board of the International Journal of Knowledge Discovery in Bioinformatics (IJKDB) since 2009, as an Editorial Board of the Computational Biology Journal since 2012 and as an Editorial Board Member of Scientific Reports NPG since 2015. Prof. Nacher Lab's bioinformatics research interests include the development and application of novel mathematical methods and algorithms in systems biology and complex biological networks.
Speech Title: "Controllability and Data Mining Integrating Transcriptome Data and Biological Networks"
Abstract: Recent studies have integrated various types of ‘omics’ data from metabolic pathways and protein interaction networks to gene expression profiles. However, integration of biological network structures with gene expression profiles have been less investigated from a controllability perspective. Here, we show some theoretical and data-driven based results on this approach. On the other hand, deep learning techniques are widely used in various fields. Here, we will also discuss our recent research progress on biological sequence analysis as well as in the integration of biological networks with gene expression data using deep learning approaches.
Dr. Zhifu Sun received his medical and pathology training in China and medical informatics training in U.S.A. He practiced surgical pathology for quite some years before switching his focus to genetics, genomics and bioinformatics fields. For the past 15 years he has worked on genetic and molecular epidemiology of lung cancer and applications of bioinformatics and data sciences to medical research and precision medicine, particularly in cancer molecular marker identification, outcome prediction and epigenomics. His recent focus expands to large and heterogeneous data integration, utilization of medical record and image data for personalized medicine. Currently, he is a Consultant and Associate Professor in the Department of Health Science Research and the Associate Director of Bioinformatics Core at Mayo Clinic, Rochester, Minnesota. He has over 110 peer-reviewed publications, with many in high impact journals such as Lancet Oncology, JCO, Cancer Research, Arch Intern Med, Ann Oncol, Bioinformatics and Genomics.
Speech Title: "Mine Big Methylome Data for Cancer Early Diagnosis and Drug Response Signatures"
Abstract: TCGA and CCLE have generated huge amount of genomics data including genome-wide DNA methylation (methylome) for thousands of primary tumors and cancer cell lines, which provide a unique opportunity for biomarker discovery. Each cancer has its own DNA methylation change hallmark and all cancers also share a common signature. These cancer specific and universal cancer markers can be used for early cancer detection by non-invasive method such as liquid biopsy. Moreover, de-methylating agents used in clinic for cancer treatment target methylome but only a fraction of patients benefit from the therapy. Identification of these patients would facilitate precision medicine. However, mining these big genomic data for the most useful information is a daunting challenge and novel and powerful approaches are needed. In this talk I will share our experience using machine learning methodology in this expedition. By analyzing 32 tumor types, we found a few hundred CpG sites that can be used for universal cancer detection with high accuracy (0.988). These universal markers can also accurately identify different tumor subtypes or origin (accuracy 0.91). DNA methylation profiles are strongly correlated with 2 de-methylation agents but not the other 2 which shed light on their different mechanisms of actions and potencies. A set of markers are identified to predict response with high accuracy.
Dr. Yan Guo is an Associate Professor at the Department of Internal Medicine, Division of Molecular Medicine, University of New Mexico. He is also serving as the director of Bioinformatics Shared Resources for the New Mexico Comprehensive Cancer Center. Before joining the University of New Mexico, Dr. Guo served as the Technical Director of Bioinformatics Core for Vanderbilt University for six years. Dr. Guo has substantial experience with NIH funded projects and has served as bioinformatician on over 30 NIH funded grants including SPORE in breast, lung and GI cancers. Dr. Guo’s research has been focused on the development of bioinformatics methodology and analysis for genomic studies and has published more than 110 manuscripts in the related fields. Dr. Guo’s latest researches heavily involve applying machine learning, deep learning techniques in biomedical research.
Speech Title: "The Applications of Machine Learning in Biomedical Researches"
Abstract: The concept of machine learning has existed for decades. With the blooming of high throughput genomic technology, machine learning methods have been frequently applied to high throughput genomic data to assist biological researches. Using this opportunity, several concrete examples of machine learning applications in big genomic data will be shown and discussed in depth. These examples include an application of machine learning techniques to identify metabolomics biomarkers for early-stage chronical kidney diseases; how deep learning methods such as convolution neural networks can be used for phenotype classification; and using deep learning method to construct a genome-wide RNA editing prediction model.
Dr. Rajeev Kanth was born in Rajbiraj, Nepal, on July 29, 1971. He received Doctor of Science (D.Sc.) in Information and Communication Technology from University of Turku, Finland, in 2013. He is currently working as a Senior Lecturer at the Savonia University of Applied Sciences, Finland where he is focusing on teaching and research on Industrial Internet of Things (IIoT). Previously, he has worked at the Indian Space Research Organization (ISRO), Ahmedabad India, Royal Institute of the Technology (KTH), Stockholm, Sweden and the University of Turku (UTU), Finland, where he has been a Researcher, Post-doctoral Researcher, and the Senior Researcher respectively. His current research interests include image and video analysis, Internet of Things, Big Data Analytics, and the Artificial Intelligence. He has published more than 45 scientific articles in peer-reviewed conference proceedings and refereed journals in the field of computer science and communication technology. He is also a recipient of a certification from Stanford Centre for Professional Development, Stanford University and has presented keynote talks, invited lectures and his research work in more than 25 countries across the world. He has been a member of IEEE communication society, IEEE cloud computing community, IEEE Earth Observation Community, and green ICT community.
Speech Title: "Image Analysis and Development of Graphical User Interface for Pole Vault Action"
In recent years, motion estimation analysis has become one of the vital research areas in sport and has attracted the interest of many researchers toward events such as swimming, pole vaulting, and hurdling. In this paper, we present a novel method for determining the step length, speed, and the feet-contact-time on the running track of a pole vault athlete using a mono-camera arrangement. The step length and step frequency are essential descriptors of the approach run in pole vaulting. The approach along a linear trajectory is familiar to many throwing and jumping events. The measurement setting and image processing, as well as the step registration stages such as the block matching and optimal flow algorithm are presented and compared to alternative methods. The validation of the step size and step frequency accuracy is provided, using manually digitized step sizes as the baseline. The proposed methodology is efficient and straightforward, providing immediate feedback to the athlete and coaches. We were also successful in building a basic graphical user interface (GUI) to illustrate pole-vaulting actions during a performance. This research could be used as an initial step for developing a fully interactive platform that is capable of yielding supportive instructions to the athletes and the coaches on a real-time basis for self-assessment and further improvement.