Deep Learners and Deep Learner Descriptors for Medical Applications

This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.

Presents recent research on all aspects of machine learning and data mining for health care Focuses on general algorithms that can handle multiple sources of complex data in medical research databases Includes various successful machine learning algorithms for health care as well as applications and descriptions of actual systems

Contenu
An Introduction to Deep Learners and Deep Leaner Descriptors for Medical Applications.- Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity. <p