This book is a valuable resource for understanding the transformative role of artificial intelligence in modern healthcare and aims to inspire continued research and collaboration across disciplines. In recent years, deep learning has emerged as a transformative technology across various fields, with medical image analysis standing out as one of its most impactful applications. This book offers a comprehensive overview of the latest developments in this fast-evolving domain, bridging foundational principles with state-of-the-art techniques that are redefining the future of medical imaging.

This book is structured in two partsPart I: Deep Learning Fundamentals and Paradigms and Part II: Advanced Deep Learning for Medical Image Analysis. The book provides in-depth coverage of essential topics, including convolutional neural networks, attention mechanisms, transformer architectures, multimodal analysis, semi-supervised learning, domain adaptation, generative models, and foundation models for large-scale pretraining.

This book is intended for a broad audience, including graduate students, academic researchers, and industry professionals in computer science, biomedical engineering, and healthcare technologies. It serves as both an introductory guide and a reference resource for those seeking to deepen their knowledge in this rapidly evolving area.

Provides a State-of-the-art deep learning-based medical image analysis Presents Paradigms and Applications from basic to professional Shows specific challenges in medical imaging and their solutions

Autorentext

Prof. Yen-Wei Chen 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. From 1991 to 1994, he was a research fellow at the Institute of Laser Technology, Osaka. From October 1994 to March 2004, he was an associate professor and a professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan. He is currently a professor at the college of Information Science and Engineering, Ritsumeikan University, Japan. Since April 2024, he has been a Foreign Fellow of the Engineering Academy of Japan. He is associate editors for the International Journal of Image and Graphics (IJIG), and the International Journal of Knowledge-based Intelligent Engineering Systems. His research focuses on computer vision, deep learning and medical image analysis. He has published more than 300 research papers in these fields. Prof. Lanfen Lin received her B.S. and Ph.D. degrees from Northwestern Polytechnical University in 1990, and 1995 respectively. She held a postdoctoral position with the department of Computer Science and Technology, Zhejiang University, China, from January 1996 to December 1997. She was an associate professor from 1998 to 2005. Now she is a full professor and the vice director of the Artificial Intelligence Institute in Zhejiang University. She is also a member of Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases.Her research interests include computer vision, medical image processing, and intelligent manufacturing. She has published more than 200 research papers in these fields. Dr. Rahul Kumar Jain received his Ph.D. degree from Ritsumeikan University, Shiga, Japan, in 2022. He has been an intern trainee at Tiwaki Co., Ltd., Japan, since 2019. He is now working as a senior researcher at the College of Information Science and Engineering, Ritsumeikan University, Japan. His research interests include computer vision, deep learning, and image processing as well as the applications of Artificial Intelligence in areas including engineering, science, computer science, healthcare, and so on.

Inhalt

Deep Convolutional Neural Networks (CNNs).- Deep CNNs for Image Classification, Object Detection, and Segmentation.- Attention and Transformer Networks.- Transformer-based Approaches for Medical Image Analysis.- Deep Learning Networks for 3D Medical Image Analysis.- Multimodal Deep Learning for Medical Image Analysis.- Semi-supervised Learning for Medical Image Analysis.- Domain Adaptation and Generalization for Medical Image Analysis.- Deep Learning Models for Medical Image Translation.- Foundation Models for Medical Image Analysis.