2020 5th International Conference on
Biomedical Signal and Image Processing
- Submission Deadline: Before July 05, 2020
- Notification of Acceptance: On July 20, 2020
- Registration Deadline: Before July 30, 2020
- Conference Date: August 21-23, 2020
Authors can attend the conference with paper publication or without publication. For paper publication, full paper should be submitted. For presentation only, abstract should be submitted.
Prof. Andrey Krylov
Lomonosov Moscow State University, Russia
Andrey Krylov received the M.S., Ph.D., and Dr. Sc. degrees from the Faculty of Computational Mathematics & Cybernetics, Lomonosov Moscow State University (CMC MSU) in 1978, 1982 (supervisor – academician Andrey Tikhonov), and 2009, respectively. He was a member of scientific staff (1981-1988), senior researcher (1988-1998), head scientist (1988-2003), associated professor (2003-2009) CMC MSU and he is currently professor, head of the Laboratory of Mathematical Methods of Image Processing (http://imaging.cs.msu.ru). During his career he worked in applied mathematics in areas of nuclear physics, physical chemistry of liquid systems, multimedia and biomedical imaging. In 1989 he received the Leninsky Komsomol Scientific Prize - the highest prize for scholars in the USSR. He has authored or coauthored over 150 published papers. He served as a reviewer for several international journals and conferences; he was in the board of international and national conferences. For a long period of time he is one of the organizers of the GraphiCon conference - the main international computer graphics, computer vision and image processing conference in Russia. He is Editor-in-Chief of the Springer journal "Computational Mathematics and Modeling" https://www.springer.com/journal/10598 (Scopus) .
Speech Title : "CNN Assisted Hybrid Algorithms for Medical Image Segmentation"
Abstract: In this report we focus on hybrid CNN assisted methods for histological image segmentation. We propose a CNN assisted interactive segmentation tool with weakly-supervised learning to accelerate the process of manual image annotation.The core of our annotation tool is a classical KNN classifier using parameters that are predicted by CNN. User annotates an image with scribbles of two types corresponding to glands and non-glands histological structures. Next the model performs label propagation to all unlabeled pixels providing user a fully annotated image build from his scribbled-based input. The user can interact with the annotation tool and add new scribbles to correct the result. The algorithm allows to reduce one image annotation time from 150 to 25-30 minutes for PATH-DT-MSU dataset (http://imaging.cs.msu.ru/en/research/histology/path-dt-msu). It seriously increases the number of fully annotated histological images necessary for the development of real diagnostic algorithms. We also present a hybrid approach to segment adjacent glands. We consider a modification of trainable active contour model with the variational parameters predicted by CNN trained in terms of structured prediction. Both hybrid approaches can be applied to a wide variety of biomedical image segmentation problems. The report reflects the BRICS2019-394 project research.
Prof. Yue Dai
East China Normal University, China
Dr. Yue Dai obtained Ph.D. degree in neurophysiology from University of Manitoba, Winnipeg, Canada in 2001, and then he did post-doctoral research in the Department of Physiology and Biophysics at University of Washington, Seattle, USA. He also received Master of Science in applied mathematics from University of Manitoba in 1996 and Bachelor of Science in computational mathematics from Yunnan University, Kunming, China in 1982. From 2003-2013 he worked as a senior research scientist in the Spinal Cord Research Center at University of Manitoba. In 2014 he was appointed to the Zijiang-scholar professor by the East China Normal University. Using combined approaches of electrophysiology and computer simulation Dr. Dai has been engaged in interdisciplinary research across neurophysiology and bioinformatics for more than 17 years. His research focuses on the cellular properties and channel mechanisms underlying locomotion and has made some important discoveries in this field.
Prof. Luonan Chen
Chinese Academy of Sciences, China
Luonan Chen received the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology, Shanghai University, and currently is the founding president of Computational Systems Biology Society of ORS China. From 2009, he also holds the affiliate professorship in the University of Tokyo. He serves as Chair of Technical Committee of Systems Biology at IEEE SMC Society, and serves as editor or editorial board member for major systems biology related journals. His fields of interest are systems biology, computational biology, and nonlinear dynamics. In recent years, he published over 250 journal papers and two monographs (books) in the area of systems biology.
Prof. Xing-Ming Zhao
Fudan University, China
Xing-Ming Zhao received his Ph.D. degree from the University of Science and Technology of China in 2005. Currently, he is a professor at the Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University. His research focuses on data mining and bioinformatics. He has published more than 70 journal papers, and is a senior member of IEEE, co-chair of IEEE SMC Technical Committee on Systems Biology and ACM SIGBio China. He is also the lead guest editor and editor member of several journals, e.g. IEEE/ACM TCBB and Neurocomputing.
Assoc. Prof. Shuaicheng Li
City University of Hong Kong, Hong Kong
CV is to be added.