ICBET 2022          Full Paper Submission Deadline: 10 March, 2022     Conference Dates: 20th - 23th April 2022      Conference Place: Tokyo, Japan



Tokyo, Japan





Prof. Irene Yu-Hua Gu

Chalmers University of Technology, Sweden

Dr. Irene Yu-Hua Gu received Ph.D. degree in electrical engineering from Eindhoven University of Technology, Eindhoven, The Netherlands, in 1992. From 1992 to 1996, she was Research Fellow at Philips Research Institute IPO, Eindhoven, The Netherlands, post dr. at Staffordshire University, Staffordshire, U.K., and Lecturer at the University of Birmingham, Birmingham, U.K. Since 1996, she has been with the Department of Electrical Engineering (previous name: Department of Signals and Systems), Chalmers University of Technology, Gothenburg, Sweden, where she has been a Professor since 2004. Her research interests include statistical image and video processing, video object tracking and recognition, machine learning and deep learning, and signal processing with applications. During the last several years her main research has been focused on biomedical image analysis and deep learning. Dr. Gu was an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, and Part B: Cybernetics from 2000 to 2005, and Associate Editor for EURASIP Journal on Advances in Signal Processing from 2005 to 2016, Editorial Board of the Journal of Ambient Intelligence and Smart Environments from 2011 to 2019. She is a Senior Area Editor for IEEE Signal Processing Letters since 2021. She was elected as the Chair of the IEEE Swedish Signal Processing Chapter from 2001 to 2004. She is a senior member of IEEE. She has coauthored over 200 papers, and has been ranked as the top 50 scientists in the field of Computer Science and Electronics in Sweden in the 6th Edition of 2020 ranking by Guide2Research team.

Speech Title: "Brain Tumor Characteristics in MRIs: Can We Train DL Networks Using Tumor Bounding Box Images without Requiring Tumor Annotation?"

Abstract: Deep learning (DL) has drawn much attention lately for medical image analysis. MR image-based brain tumor analysis is important for determining treatment strategies. Such analysis includes MRI-based tumor molecular subtype classification, tumor segmentation, and many more. Most existing DL methods require training by annotated medical data, which puts high demands on medical experts. It is desirable to seek alternative approaches for training brain tumors on MRIs without using (or using less) tumor annotation, while still maintain high DL performance on the test sets. In this talk, we present an alternative paradigm for training DL networks, by using tumor bounding boxes instead of annotated tumors on MRIs. Despite bounding boxes are widely used for visual image analysis in computer vision, its usage in medical images remains an open issue. Two examples are presented, one is for brain tumor subtype classification, the other for brain tumor segmentation. Their performance is then compared with the same network trained on annotated MRIs. These studies have demonstrated that such paradigm is feasible for MRI-based classification and segmentation, and is a tradeoff between significantly reduced (or without) tumor annotation and a slightly decreased test performance.


Assoc. Prof. Md. Altaf-Ul-Amin

Nara Institute of Science and Technology, Japan

Md. Altaf-Ul-Amin received B.Sc. degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, M.Sc. degree in Electrical, Electronic and Systems Engineering from Universiti Kebangsaan Malaysia (UKM) and PhD degree from Nara Institute of Science and Technology (NAIST), Japan. He received the best student paper award in the IEEE 10th Asian Test Symposium. Also, he received two other best paper awards as a co-author of journal articles. He previously worked in several universities in Bangladesh, Malaysia and Japan. Currently he is working as an associate professor in Computational Systems Biology Lab of NAIST. He is conducting research on Network Biology, Systems Biology, Cheminformatics and Biological Databases. He published around 60 peer reviewed papers in international journals and conference proceedings. Google scholar citation index of his publications is currently more than 6700.

Speech Title: "Applications of KNApSAcK Database and DPClus Algorithm: Plants to Metabolites to Target Proteins in the Context of Jamu Medicines and Disease Pathway Prediction"

Abstract: Initially, we developed KNApSAcK as a species-metabolite relational database and subsequently, inspired by its popularity we extended it to KNApSAcK family databases by adding different types of omics data together with data regarding edible plants and traditional medicines mainly focusing human health care and ecology. Previously we also developed graph clustering algorithms DPClus and DPClusO, which we and many other researchers applied to analysis of versatile omics data. In the present talk, first, I will briefly focus on the KNApSAcK database and the DPClus algorithm. Then I will discuss a new method to predict the relation between plant and disease using network analysis and supervised clustering based on Jamu formulas. Jamu is the common name of Indonesian traditional medicines. Next, I will extend the talk on the analysis for predicting Jamu efficacy based on metabolite composition and identifying important metabolites by applying various machine learning techniques and algorithms including support vector machine (SVM) and random forest. I will then focus on prediction of target proteins by Jamu metabolites. Finally, I will discuss application of DPClusO algorithm in finding inflammatory bowel disease(IBD), schizophrenia and bipolar disorder related genes and pathways.



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