Recent Advances in Deep Learning for Medical Image Analysis

Overview

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.

Structure

This book is structured in two parts—Part I: Deep Learning Fundamentals and Paradigms and Part II: Advanced Deep Learning for Medical Image Analysis.

Topics Covered

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.

Intended Audience

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.