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Springer Generative Machine Learning in Medical Imaging Book

Amazon

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Description

Explore the frontier of medical image computing with this comprehensive guide from Springer. This book provides a detailed look at generative modeling techniques designed for the specific requirements of medical imaging. It covers advanced models including GANs, VAEs, and diffusion models to explain how they drive progress in image synthesis, reconstruction, and enhancement. Readers will learn how these technologies apply to essential imaging modalities such as MRI, CT, and ultrasound. The text demonstrates how generative models improve image quality and provide vital support for data augmentation when dealing with scarce datasets. This is an essential resource for understanding how machine learning transforms modern radiology and medical imaging workflows.

Key Features

This Springer publication offers an in-depth overview of generative models like GANs, VAEs, and diffusion models.

Learn how generative modeling enables breakthroughs in medical image synthesis, reconstruction, and enhancement.

The content covers practical applications across diverse modalities including MRI, CT, and ultrasound imaging.

Understand how these machine learning techniques help improve image quality for better diagnostic results.

Discover how generative models support data augmentation to address the challenges of scarce medical datasets.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
13 March 2025
Listed Since
12 November 2024

Barcode

No barcode data available

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