Choice Recommended Title, January 2021
This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today.
After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts.
In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored.
The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field.
- An accessible yet detailed overview of the field
- Explores a hot and growing topic
- Provides an interdisciplinary perspective
Chapter 1. Fundamentals of Machine Learning
Chapter 2. Introduction to Deep Learning
Chapter 3. Applying AI in Medical Imaging
Chapter 4. Designing AI Systems for the Clinical Practice
Chapter 5. Future Perspectives
"Artificial intelligence is on the cusp of integration into clinical practice, so this work by Morra (Polytechnic Univ. of Turin) and colleagues Delsanto and Correale is timely and complements other work in the field. In only 112 pages, the authors provide a readable, high-level description of machine learning techniques and cover areas ranging from computer-aided detection to neural networks and deep learning. The book provides an impressive list of 264 references, current to the time of publication.
In addition to describing specific examples in the form of case studies, the authors provide a valuable discussion of the challenges that need to be overcome to introduce AI into standard medical practice and suggest ways to achieve this. The writing is concise and well organized…While the math included in the introductory chapters is not for the uninitiated, the rest of the book will be highly accessible to students at the graduate level and certainly to clinicians. Recommended. Graduate students, faculty, and professionals."
—L. S. Cahill, Memorial University of Newfoundland in CHOICE (January 2021)