Kyoto, Japan

Keynote Speakers

 

Keynote Speaker I

Prof. Chin-Chen Chang, IEEE and IET Fellow

Feng Chia University, Taiwan

 

Professor Chang has worked on many different topics in information security, cryptography, multimedia image processing and published several hundreds of papers in international conferences and journals and over 30 books. He was cited over 40,340 times and has an h-factor of 93 according to Google Scholar. He served as Honorary Professor, Consulting Professor, Distinguished Professor, Guest Professor at over 50 academic institutions and received Distinguished Alumni Award's from his Alma Master's. He also served as Editor or Chair of several international journals and conferences and had given almost a thousand invited talks at institutions. He has been the Editor-in-Chief of Information Education. He founded the Chinese Cryptography and Information Security Association, accelerating information security the application and development and consulting on the government policy. He is also the recipient of several awards, including the Top Citation Award from Pattern Recognition Letters, Outstanding Scholar Award from Journal of Systems and Software, and Ten Outstanding Young Men Award of Taiwan. He was elected as a Fellow of IEEE in 1998, a Fellow of IET in 2000, a Fellow of CS in 2020, and an AAIA Fellow in 2021 for his contribution in the area of information security.

 

Speech Title: "Borrowing from Nature to Conceal Information "

 

Abstract: Steganography is the science of secret message delivery using cover media. A digital image is a flexible medium used to carry a secret message because the slight modification of a cover image is hard to distinguish by human eyes. In this talk, I will introduce some novel steganographic methods based on different magic matrices. Among them, one method that uses a turtle shell magic matrix to guide cover pixels’ modification in order to imply secret data is the newest and the most interesting one. Experimental results demonstrated that this method, in comparison with previous related works, outperforms in both visual quality of the stego image and embedding capacity. In addition, I will introduce some future research issues that derived from the steganographic method based on the magic matrix.

 

 

Keynote Speaker II

Prof. Zhifu Sun

Mayo Clinic, USA

 

Dr. Zhifu Sun is a Professor and Consultant in the Department of Quantitative Health Sciences, Mayo Clinic. His research interests are to apply bioinformatics to medical research/practice, particularly in cancer etiology, molecular marker identification for outcome prediction, and personalized medicine. His major focus has been epigenomics (DNA methylation, histone modification, chromatin interaction, miRNA and long non-coding RNAs) and multi-platform genomic feature integration. He has over 120 peer-reviewed publications, with many in high impact journals. He is an editorial board member for Epigenomics and an associate editor for BMC Cancer and Frontiers in Oncology.

 

Speech Title: "Deep Learning in Molecular Marker Prediction from Whole Slide Histology Image: State of Art or Hyper "

 

Abstract: The adoption of digital pathology with whole slide images (WSIs) has been revolutionizing clinical practice and research. With growing popularity of deep learning, investigators have demonstrated its great potentials to facilitate disease diagnosis and outcome predictions. One of such interests is to use WSIs to predict molecular markers (such as somatic gene mutation or gene/protein expression) which can be used to stratify patients for targeted therapy or customized management. In this talk I will highlight the current progress in the field and share our experience with PDL1 expression prediction from WSIs in non-small cell lung cancer, which can potentially bypass a separate clinical testing to select patients with anti-PDL1 inhibitor treatment.

 

 

Invited Speakers

 

Invited Speaker I

Asst. Prof. Yanshan Wang, FAMIA

University of Pittsburgh, USA

 

Yanshan Wang, PhD, FAMIA is vice chair of research and assistant professor with a primary appointment in the Department of Health Information Management, School of Health and Rehabilitation Sciences, and secondary appointments in the Intelligent Systems Program, School of Computing and Information, and Department of Biomedical Informatics, School of Medicine, at the University of Pittsburgh. His research interests focus on artificial intelligence (AI), natural language processing (NLP) and machine learning methodologies and applications in health care. His research goal is to leverage different dimensions of data and data-driven computational approaches to meet the needs of clinicians, researchers, and patients. He joined Pitt from the Mayo Clinic where he is still holding an adjunct Assistant Professor position. He has published over 60 peer-reviewed articles in high-impact medical informatics journals and conferences. Dr. Wang is also active in organizing conference workshops and shared tasks in the medical informatics community, including the international Health NLP workshops and the national NLP clinical challenge (n2c2).

 

Speech Title: "Natural Language Processing for Clinical Excellence: The State of Practices, Opportunities, and Challenges"

 

Abstract: Rapid growth in adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets. Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice. In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents. Meanwhile, a number of clinical NLP systems, such as MedLEE, MetaMap/MetaMap Lite, cTAKES, MedTagger, and i2b2 have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports. This talk will walk through some successful applications of NLP techniques in the clinical domain with potential opportunities and challenges.

 

 

 

 

 

Copyright © DMIP 2022