Deep Learners and Deep Learner Descriptors for Medical Applications / / edited by Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (286 pages)
• Disciplina: 610.28
• Soggetto topico: Computational intelligence; Artificial intelligence; Health informatics; Biomedical engineering; Computational Intelligence; Artificial Intelligence; Health Informatics; Biomedical Engineering and Bioengineering
• Persona (resp. second.): NanniLoris; BrahnamSheryl; BrattinRick; GhidoniStefano; JainLakhmi C
• Nota di bibliografia: Includes bibliographical references.
• Sommario/riassunto: 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. .
• Titolo autorizzato: Deep Learners and Deep Learner Descriptors for Medical Applications
• ISBN: 3-030-42750-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483851403321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library, . 1868-4394 ; ; 186