This book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-25), held in Solin, Croatia, on June 2527, 2025. Covering several key areas, including digital IT architecture in healthcare; advanced ICT for medicine and healthcare; biomedical engineering, trends, research, and technologies; and healthcare support systems, this book is a valuable resource for researchers, managers, industrialists, and anyone wishing to gain an overview of the latest research in these fields.

Presents research works in the field of innovation in medicine and healthcare Provides original works presented at KES-InMed 2025 held in Solin, Croatia Serves as a reference for researchers and practitioners in academia and industry

Autorentext

Dr. Yen-Wei Chen was born in Hangzhou, China, in 1962. He 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. He was Research Fellow at the Institute of Laser Technology, Osaka, from 1991 to 1994. From October 1994 to March 2004, he was Associate Professor and Professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently Professor at the College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan. He is also Visiting Professor at the College of Computer Science and Technology, Zhejiang University, Hangzhou, China. He was Visiting Scholar at Oxford University, Oxford, UK, in 2003 and at Pennsylvania State University, Pennsylvania, USA, in 2010. His research interests include medical image analysis and pattern recognition. 
 
Prof. Satoshi Tanaka got his Ph.D. in theoretical physics at Waseda University, Japan, in 1987. After experiencing Assistant Professor, Senior Lecturer, and Associate Professor at Waseda University and Fukui University, he became Professor of Ritsumeikan University in 2002. His current research target is computer visualization of complex 3D shapes such as 3D scanned cultural heritage objects, inside structures of a human body, and fluid simulation results. Recently, he was President of JSST (Japan Society for Simulation Technology) and President of ASIASIM (the Federation of Asia Simulation Societies). Currently, he is working as Vice-President of the VSJ (Visualization Society of Japan) and Cooperation Member of Japan Science Council. He is Best Paper Winners at Asia Simulation Conference 2012, Journal of Advanced Simulation in Science and Engineering in 2014, and many others.

Dr. Robert Howlett is Executive Chair of KES International, a non-profit organization that facilitates knowledge transfer and the dissemination of research results in areas including intelligent systems, sustainability, and knowledge transfer. He is Visiting Professor at Bournemouth University in the UK. His technical expertise is in the use of intelligent systems to solve industrial problems. He has been successful in applying artificial intelligence, machine learning, and related technologies to sustainability and renewable energy systems; condition monitoring, diagnostic tools and systems; and automotive electronics and engine management systems. His current research work is focused on the use of smart microgrids to achieve reduced energy costs and lower carbon emissions in areas such as housing and protected horticulture.

Dr. Lakhmi C. Jain, Ph.D., M.E., B.E. (Hons), Fellow (Engineers Australia) is with the University of Technology Sydney, Australia, and Liverpool Hope University, UK. Professor Jain serves the KES International for providing a professional community the opportunities for publications, knowledge exchange, cooperation, and teaming. Involving around 5,000 researchers drawn from universities and companies worldwide, KES facilitates international cooperation and generate synergy in teaching and research. KES regularly provides networking opportunities for professional community through one of the largest conferences of its kind in KES.

Inhalt

Clinical Feature-Guided Representation Learning on Whole Slide Images for Hepatocellular Carcinoma Recurrence Prediction.- A Teacher-student Learning Approach Using Gene and CT Images for Recurrence Prediction of Non-small Cell Lung Cancer.- Enhancing Large Language Model Efficiency with MCE (Main Content Extractor): Token Optimization and Streamlined Content Integration.- IHC Pathological Image Generation Based on Multi-Path Reversible Feature Transformation and Multi-Scale HE Staining Transfer.- Comparison of Deep Learning Architectures for Generalizable Lung Nodule Segmentation.