Machine Learning in Medicine

Machine learning and deep learning techniques are used in biomedical imaging to improve computer-aided diagnosis. They cover thoracic, abdominal, brain, and retinal imaging and include new and emerging methods. Contributions from leading experts are featured.

Format: Paperback / softback
Length: 292 pages
Publication date: 25 September 2023
Publisher: Taylor & Francis Ltd

Machine learning and deep learning techniques have revolutionized the field of biomedical imaging, enabling accurate and efficient diagnosis of a wide range of medical conditions. This comprehensive guide provides a detailed overview of these techniques, covering thoracic imaging, abdominal imaging, brain imaging, and retinal imaging. It also explores new and emerging methods in machine learning, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), which have shown remarkable promise in medical image analysis.

The book features contributions from leading experts in the field, including researchers, physicians, and engineers, who share their insights and expertise. It provides a practical guide for practitioners and researchers, offering tools and resources to improve computer-aided diagnosis and enhance patient care.

Thoracic imaging involves the use of imaging techniques to visualize the organs and structures within the chest cavity. It includes modalities such as chest X-rays, CT scans, and MRI scans. Machine learning and deep learning techniques have been applied to thoracic imaging to improve the accuracy and efficiency of diagnosis. For example, CNNs can be used to analyze chest X-rays and identify abnormalities such as lung cancer, pneumonia, and pleural effusion.

Abdominal imaging involves the use of imaging techniques to visualize the organs and structures within the abdominal cavity. It includes modalities such as ultrasound, CT scans, and MRI scans. Machine learning and deep learning techniques have been applied to abdominal imaging to improve the accuracy and efficiency of diagnosis. For example, GANs can be used to generate realistic images of abdominal organs, which can be used to aid in the diagnosis of diseases such as kidney stones, liver tumors, and appendicitis.

Brain imaging involves the use of imaging techniques to visualize the brain and its structures. It includes modalities such as MRI scans, CT scans, and PET scans. Machine learning and deep learning techniques have been applied to brain imaging to improve the accuracy and efficiency of diagnosis. For example, CNNs can be used to analyze brain images and identify abnormalities such as tumors, stroke, and Alzheimer's disease.

Retinal imaging involves the use of imaging techniques to visualize the retina and its structures. It includes modalities such as fundus photography, OCT scans, and ERG tests. Machine learning and deep learning techniques have been applied to retinal imaging to improve the accuracy and efficiency of diagnosis. For example, GANs can be used to generate realistic images of the retina, which can be used to aid in the diagnosis of diseases such as diabetic retinopathy, macular degeneration, and glaucoma.

In addition to covering these specific imaging modalities, the book also discusses new and emerging methods in machine learning, such as transfer learning and self-supervised learning. Transfer learning involves the use of pre-trained models to adapt to new datasets, while self-supervised learning involves training models to learn from unlabeled data. These methods have shown promise in medical image analysis, particularly in the field of radiology, where there is a large amount of data and a need for efficient and accurate diagnosis.

The book also includes case studies and examples that illustrate the practical applications of machine learning and deep learning techniques in biomedical imaging. These examples demonstrate how these techniques can be used to improve the accuracy and efficiency of diagnosis, as well as to identify new patterns and abnormalities that may not have been previously recognized.

Overall, Machine Learning and Deep Learning Techniques in Biomedical Imaging is a comprehensive and authoritative guide to the latest developments in this field. It is an essential resource for practitioners, researchers, and students interested in advancing the field of medical imaging and improving patient care.

Weight: 576g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032039855