ICBET 2024          Full Paper Submission Deadline: 20 April, 2024     Conference Dates: 14th - 17th June 2024      Conference Place: Seoul, South Korea



Seoul, South Korea





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: "Federated Deep Learning and its Application to Brain Cancer Molecular Subtype Prediction from MRIs"

Abstract: One of the most common types of brain cancer is glioma. Identification of glioma subtypes is essential for clinicians to decide treatment strategies. If MRI brain scans show tumors, biopsies are usually followed. Previous studies show that deep learning-based classifiers are promising for predicting glioma subtypes from MRI scans of new patients without using biopsies. These classifiers are formed by deep networks trained by MRI datasets with tumor subtypes as annotations. Many challenges remain for such classifiers on achieving high performance useful to clinical applications. Apart from requiring large MRI training datasets or combining multiple small MRI datasets, data protection and privacy from different hospitals/regions/countries pose heavy constrains on sharing training datasets. Federated learning offers the possibility that each hospital can hold its own training dataset without sharing, while still obtain a local classifier with nearly the same performance as that of a centrally trained classifier by using all datasets. In this talk, we first review several federated deep learning approaches, followed by dedicated federated learning for glioma classification especially for glioma subtypes. We show results that federated learned classifiers may achieve almost similar performance as that of centrally learned classifiers. This is encouraging for further AI/DL research towards clinical applications.

Prof. Stephen Kwok-Wing Tsui (h-index: 58)

The Chinese University of Hong Kong, Hong Kong

Stephen Kwok-Wing Tsui is currently a Professor and the Associate Director (Research) in the School of Biomedical Sciences. He is also the Director of Hong Kong Bioinformatics Centre in the Chinese University of Hong Kong (CUHK). In 1995, he received his PhD degree in Biochemistry at CUHK. He was then appointed as an Assistant Professor in the Biochemistry Department in 1997 and promoted to the professorship in 2004. He was also a former member of the International HapMap Consortium and worked on the single nucleotide polymorphisms of human chromosome 3p. During the SARS outbreak in 2003, his team was one of the earliest teams that cracked the complete genome of the SARS-coronavirus and facilitated the emergence of real-time PCR assay for the virus. Totally, he has published more than 240 scientific papers in international journals, including Nature, Nature Machine Intelligence, New England Journal of Medicine, Lancet, PNAS, Nucleic Acids Research, Genome Biology and Bioinformatics. His h-index is 58 and the citations of his publications are over 20,000. His major research interests are next generation sequencing, bioinformatics and metagenomics in human diseases.

Speech Title: "Genomics Study Reveals Insights into the Divergent Evolution and Comprehensive Allergen Profiles of Astigmatic Mites"

Abstract: Highly diversified astigmatic mites comprise many medically important human household pests such as house dust mites causing ~ 1-2% of all allergic diseases globally. However, their evolutionary origin and diverse lifestyles have not been illustrated at the genomic level, which hampers allergy prevention and our exploration of these household pests. Using six high-quality assembled and annotated genomes, this study thoroughly explored the divergence of Acariformes and the diversification of astigmatic mites. Within astigmatic mites, a wide range of gene families rapidly expanded via tandem gene duplications. Gene diversification after tandem duplications provides many genetic resources for adaptation to sensing environmental signals, digestion, and detoxification in rapidly changing household environments. Throughout the evolution of Acariformes, massive horizontal gene transfer events occurred in gene families enable detoxification and digestive functions and provide perfect drug targets for pest control. This genomics study sheds light on the divergent evolution and quick adaptation to human household environments of astigmatic mites and provides insights into the genetic adaptations and even control of human household pests. Moreover, in this talk an innovative and efficient way to unveil the comprehensive allergen profiles of astigmatic mites will be described.



Prof. Dilek Çökeliler Serdaroğlu

Başkent University, Turkey

Dilek Çökeliler Serdaroğlu is a professor and chair in the Department of Biomedical Engineering at Baskent University, Ankara, Turkey (Accredited by MUDEK). Dr. Çökeliler received his Ph.D. from Hacettepe University, Ankara, Turkey including one year international doctoral training experience study entitled “plasma aided immunosensor fabrication” in Charles University, Macromolecular Physics Department in Czech Republic. Moreover, she won “Post Gradual Study Support” Funded by The Ministry of Education of Czech Republic, at the Department of Physics, J.E. Purkyne University. She was post doctoral researcher in the Biological Systems Engineering Department, University of Wisconsin Madison,USA. Now, her research and teaching activities at Başkent University have emphasized on both various aspects of plasma surface modifications of biomaterials and nanofiber production with electrospinning technique / nanomaterial-based mass sensitive sensors. She is the recipient of several international patent awards (number: +8) including the L’ORÉAL Awards for Women in Science in 2009 In addition, she is a regular lecturer in the Intensive Summer Course: Biomedical Engineering in an International Perspective in Jade Hochschule Wilhelmshaven, Applied Engineering Science Department, Germany for 15 years.

Speech Title: "Glow Discharge Plasma Technology for Biomedical Applications and Integration Design of Experiment Model for In-Vitro and In-Vivo Test"

Abstract: This presentation evaluates the performance of a focused plasma-based surgical prototype device for use in spine discectomy surgery, including identifying active device operating conditions through engineering models and in-vitro tests. It also discusses experiences related to antibacterial efficacy, creating an appropriate in-vivo wound model, and evaluating the healing process to demonstrate the effect of plasma irradiation on anastomosis. Plasma is a partially ionized gas mixture with highly reactive ions and electrons, neutral atoms, electric fields, reactive molecules, induced species, and UV radiation resulting from exposing a substance to solid energy in the gas direct plasma discharge application to living cells, bacteria, or tissues, studied for its potential in disinfection, healing, and cancer treatment. The design of the experiment analysis is crucial in engineering, improving production processes and optimization, and revealing changes in output due to input variable changes. Experimental design methods are used to understand factors like plasma charge neutralization, observe optimal factor combinations, show statistical significance, and determine successful factor levels. This presentation includes in-vitro and in-vivo experiences, using a complete factorial experimental design in plasma-based device development.

Assoc. Prof. Giovanni Pappalettera

Polytechnic University of Bari, Italy

Giovanni Pappalettera is an Associate Professor at Polytechnic University of Bari, where he also lectures in the Bachelor Degree in Industrial Design and the Master Degree of Mechanical Engineering. He earned his PhD in Mechanical and Biomechanical Design at Politecnico di Bari and in Biomechanics and Bioengineering at Scuola Interpolitecnica di Dottorato di Torino. His primary research interests include material characterization and experimental mechanics, encompassing optical methods, acoustic emission, and residual stress analysis. He is actively involved in biomechanics research including ultrasound-induced fatigue effects on cells, mechanical characterization of dental devices, and 3D dental element reconstruction. In 2007 and 2008, he was a visiting researcher at Politechnika Warsawska – Warszawa, and in 2009, he was a visiting researcher at the Northern Illinois University - DeKalb. Giovanni serves on the PhD committee in Mechanical and Management Engineering at Politecnico di Bari. He has authored over a hundred papers in international journals and conference proceedings and is a co-inventor of three Italian patents. He is also a co-author of three books and a member of international associations, including the Society of Experimental Mechanics, the European Society of Experimental Mechanics, and the Italian Association of Stress Analysis. Since 2003, he has been a research associate at the National Institute of Nuclear Physics (INFN).

Speech Title: "Effects of OUT (Onco-Ultrasound-Tripsy) in-vitro Treatment on Cancer and Healthy Cells: Application to U937 Human Histiocytic Lymphoma Cells"

Abstract: In recent years, research has made many progresses in understanding and treating cancer. Different types of tumours can now be treated successfully, allowing a long-life expectancy after the disease. However, the interest towards alternative clinical approaches is alive and one of the current challenges consists in identifying treatments able to act selectively on tumour, without harming the surrounding environment. Several studies have highlighted the ability of low-intensity ultrasound to selectively damage tumour cells, based on the different mechanical properties that they present compared to healthy cells. In this study, we analyzed the ability of ultrasound to damage U937 Human Histiocytic Lymphoma cells, without altering the viability of the corresponding healthy Human CD14+ Monocytes cells, used as control. Different sonication parameters were tested, to identify the conditions that allow the best results in terms of selective death of U937 cancer cells to be achieved. Informativa Privacy - Ai sensi del Regolamento (UE) 2016/679 si precisa che le informazioni contenute in questo messaggio sono riservate e ad uso esclusivo del destinatario. Qualora il messaggio in parola Le fosse pervenuto per errore, La preghiamo di eliminarlo senza copiarlo e di non inoltrarlo a terzi, dandocene gentilmente comunicazione. Grazie. Privacy Information - This message, for the Regulation (UE) 2016/679, may contain confidential and/or privileged information. If you are not the addressee or authorized to receive this for the addressee, you must not use, copy, disclose or take any action based on this message or any information herein. If you have received this message in error, please advise the sender immediately by reply e-mail and delete this message. Thank you for your cooperation.

Assoc. Prof. Guanghui (Richard) Wang

Toronto Metropolitan University, Canada

Guanghui (Richard) Wang received the Ph.D. degree in engineering from the University of Waterloo, Waterloo, ON, Canada, in 2014. He is currently an Associate Professor with the Department of Computer Science, Toronto Metropolitan University (TMU), Toronto, ON. He is also the director of the Computer Vision and Intelligent Systems Laboratory at TMU. From 2014 to 2020, he was an Assistant Professor and then an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas (KU), Lawrence, KS, USA. He has authored one book “Guide to Three Dimensional Structure and Motion Factorization”, published by Springer-Verlag. He has authored or co-authored more than 180 papers in peer-reviewed journals and conferences. His research interests include computer vision, image analysis, machine learning, and intelligent systems.

Speech Title: "Polyp Segmentation from Colonoscopy Images Using Enhanced Neural Networks"

Abstract: Colonoscopy is a crucial procedure for detecting colorectal polyps, which stand as the primary precursors to colorectal cancer. However, accurately segmenting polyps presents a significant challenge due to their diverse shapes, sizes, colors, and texture variations. To address these challenges and enhance polyp segmentation performance, we propose two enhanced neural network structures. The first network enhances semantic information through a novel Semantic Feature Enhancement Module (SFEM) and an Adaptive Global Context Module (AGCM). By integrating these modules, we progressively refine feature quality across layers, thereby enhancing the final feature representation. The second network introduces a novel Fuzzy Attention module, designed to prioritize difficult pixels, particularly those near the boundaries that pose a greater challenge for prediction. This attention module can be seamlessly incorporated into any backbone network. We evaluated its efficacy with three backbone networks: Res2Net, ConvNext, and Pyramid Vision Transformer. Our proposed approaches are rigorously evaluated across five colonoscopy datasets, showcasing superior performance compared to other state-of-the-art models.

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 90 peer reviewed papers in international journals and conference proceedings. Current google scholar citation index of his publications is more than 7900.

Speech Title: "Computational Approaches to Predict Natural Antibiotics based on Traditional Herbal Medicines"

Abstract: Antibiotic resistance is a major public health threat and there is an urgent need for new antibiotics. Traditional herbal medicine systems, such as Jamu, Unani, and Traditional Chinese Medicine, have been used for centuries to treat bacterial infections. Machine learning methods have been shown to be effective for predicting potential natural antibiotic candidates based on traditional herbal medicine systems. In this study, we used machine learning methods to predict potential natural antibiotic candidates at plant and metabolite levels. We evaluated different machine learning algorithms and preprocessing techniques to obtain the best prediction accuracy. For Jamu, we achieved an accuracy of 91.10% using the Random Forest model. For Unani, we achieved an accuracy of 83% using a multilayer perceptron model with SMOTE preprocessing. In total, we predicted 42 potential plant candidates and 201 candidate metabolites as potential natural antibiotics. Many of these candidates have been validated based on published literature mentioning their antibacterial properties. Some others are structurally similar to known antibiotics. Our findings suggest that machine learning methods can be used to effectively predict potential natural antibiotic candidates utilizing traditional herbal medicines. This approach has the potential to accelerate the development of new antibiotics to combat antibiotic-resistant pathogens.

Prof. Wenqiao (Wayne) Yuan

North Carolina State University, USA

Dr. Wenqiao (Wayne) Yuan is currently a professor at the Department of Biological and Agricultural Engineering, North Carolina State University (NCSU), Raleigh, NC, USA. He received a Ph.D. degree in Biological and Agricultural Engineering from the University of Illinois at Urbana-Champaign, Urbana, IL, USA. He has been the chair and vice chair of several technical committees of American Society of Agricultural and Biological Engineers (ASABE), and is an associate editor of Journal of the ASABE, International Journal of Agricultural and Biological Engineering, and Applied Engineering in Agriculture. Dr. Yuan’s research is mainly focused on bioenergy and bioproducts, such as microalgae culture and bioprocessing, green agriculture development, biomass thermochemical and biochemical conversion, and microbial ad bio-fuel cells. Dr. Yuan has published more than 200 papers and 4 book chapters, with numerous invited speeches worldwide. He received the “CAREER Award” in 2010 from US National Science Foundation, the “New Holland Young Researcher Award” in 2012, the “Engineering Concept of the Year Award” in 2016 from ASABE, and the “Research Fellowship for Experienced Researchers” from Alexander von Humboldt Foundation (Germany) in 2017. He has also received nearly a dozen other prestigious international and national awards and honor.

Speech Title: "Biochar and its Potential Biomedical Applications"

Abstract: Biochar is a carbon-rich material resulted from the thermal conversion of biomass. It can improve environmental quality when used for pollutant removal in liquid or gas media, soil conditioning, or as a long-term storage of carbon. It has been found to have at least 50 different uses. In this presentation, a non-traditional use of biochar in biomedical engineering is discussed. Mesoporous carbon particles (MCP) were generated from activated biochar. Then Glucose oxidase (GOx) and catalase (CAT) were self-assembled on the MCPs to form a GOx/CAT/MCP composite, which was then dispersed in Nafion solution and layered on carbon cloth to fabricate a flexible 3-D bio-anode possessing extremely high electron transfer rate. Such bio-anode designed via the simple, bio-compatible, and multi-enzyme self-assembly approach displayed exceptional electrochemical performance for the fabrication of glucose-based bio-fuel cells or biosensors, which has high potential to be a self-sustained bio-battery for implantable biomedical devices or for human health monitoring, especially for diabetic patients.


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