Nanni Loris et al (eds.). Deep Learners and Deep Learner Descriptors for Medical Applications

Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain. (Editors) — Springer, 2020. — 286 p. — ISBN: 978-3-030-42750-4 (eBook).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.