Medical Image Analysis with Deep Learning

Architectures, Implementations, and Prospects
Erscheinungsjahr 2026
ISBN: 978-981-9225-47-7
Verlag: Springer

Buch, Englisch, 214 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Computational Intelligence Methods and Applications

Rapid advances in artificial intelligence have established medical image analysis as a cornerstone of intelligent healthcare. Deep learning techniques, including convolutional neural networks (CNNs), graph convolutional networks (GCNs), multi-layer perceptrons (MLPs), and vision transformers (ViTs) architectures, substantially enhance performance in medical image classification and segmentation. This progress advances diagnostic accuracy, robustness, and efficiency.

This book systematically surveys deep learning models for medical image analysis. It documents the evolution from MLPs and CNNs to hybrid attention architectures, with technical analysis of 9 recent methodologies. Core topics cover: multi-scale feature fusion, multi-branch CNN structures, graph-based feature modeling, region-aware attention mechanisms, adaptive positioning modules, and lightweight model design.

This book addresses: (1) MLP-based models for disease classification; (2) integrated CNN and ViT approaches for spatially contextualized learning; (3) GCNs for topological and relational representation; and (4) lightweight models for efficient deployment under resource constraints. Each chapter examines representative publications, summarizing methodological innovations, architectures, experimental results.

This work integrates theory with implementation, serving as a reference for researchers and professionals in medical imaging, computer-aided diagnosis, and biomedical AI. It establishes foundations for current deep learning paradigms and future development.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Liejun

Guo, Zhiqing

Tao, Xiaoming

Hao, Yatong

Li, Keqin

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

.- Introduction.

.- Technical Evolution in Medical Image Analysis.

.- Multi-scale and Fusion-enhanced MLPs for Medical Image Classification.

.- Hybrid CNN Transformer Models for Regional Representation in Medical Imaging Analysis.

.- Graph Convolutional Networks for Structural Feature Integration.

.- Lightweight Models for Efficient Medical Image Analysis.

.- Conclusion and Future Prospects.

Über Autor(innen)

Professor Liejun Wang works on computer vision, remote sensing, and medical imaging. He has publications in IEEE TIP, IEEE TGRS, IEEE TCSVT, Applied Soft Computing, Expert Systems with Applications, and other journals.

Associate Professor Zhiqing Guo has a strong research profile with multiple first-author publications in top-tier journals like IEEE TIFS and TMM. He serves as a reviewer for numerous leading academic journals and conferences.

Professor Xiaoming Tao received the National Science Fund for Distinguished Young Scholars and the China Young Women Scientists Award. She served as Workshop General Co-Chair of IEEE INFOCOM 2015 and has been an editor of the Journal of Communications and Information Networks and China Communications since 2016.

PhD Student Yatong Hao conducts research at the intersection of computer science and biomedicine. He has publications in Biomedical Signal Processing and Control and Briefings in Bioinformatics.

Professor Keqin Li is a Fellow of AAAS/IEEE/AAIA/ACIS/AIIA and a Member of the European Academy of Sciences and Arts and Academia Europaea. He has 1,130+ publications; Scopus ranks #2 (single-year) and #4 (career-long) in parallel and distributed computing; awards include IEEE TCCLD ’22, IEEE TCSVC ’23, and IEEE Region 1 Technological Innovation (Academic) ’23.