دانلود کتاب کاربردهای یادگیری عمیق در بخش بندی تصاویر پزشکی

The field ofmedical image segmentation has seen tremendous advancements over the past decade, driven largely by the rapid development of deep learning technologies. These advancements have enabled the medical community to achieve levels of precision and efficiency in image analysis that were once thought impossible. Recognizing the profound impact of these technologies, we set out to create a comprehensive resource that would serve as both an introduction to the field and a guide to the latest research and techniques. Deep Learning Applications in Medical Image Segmentation was conceived with the vision of providing readers with a thorough understanding of how deep learning is revolutionizing the way we approach medical image segmentation. This book brings together contributions from experts in the field, each offering their unique insights into various aspects of deep learning and its application to medical imaging. The book is organized to guide readers from foundational concepts to advanced techniques. It begins with an introduction to the basics of medical image segmentation, including key datasets, data augmentation methods, and evaluation metrics. As readers progress, they will encounter discussions on traditional segmentation approaches, providing a historical context that highlights the evolution of methodologies leading to the current state-of-the-art deep learning models. In subsequent chapters, the book delves into specific applications of deep learning in medical imaging, covering critical areas such as brain tumor segmentation, retinal vessel segmentation, and early detection of diabetic retinopathy. These chapters not only explore the techniques employed but also address the challenges and limitations that researchers face in these specialized domains. The book also covers cutting-edge topics such as the use of generative adversarial networks (GANs) for image segmentation and collaborative models for cell image segmentation. By including these advanced topics, we aim to provide readers with a forward-looking perspective on the future of medical image segmentation. We believe this book will serve as a valuable resource for a wide audience, including researchers, practitioners, and students. It is designed to equip readers with both the theoretical knowledge and practical insights needed to navigate the rapidly evolving landscape of medical image segmentation. Our hope is that this book will inspire further research and innovation in this vital field, ultimately contributing to improved healthcare outcomes. As editors, we are deeply grateful to the contributing authors, reviewers, and all those who have supported this project. Their expertise, dedication, and collaborative spirit have been instrumental in bringing this book to fruition. We trust that readers will find this book to be an informative and enriching resource that will aid them in their professional development and research endeavors.