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Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: booksXpress, Bayonne, NJ, U.S.A.
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Soft Cover. Condition: new.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: SpringBooks, Berlin, Germany
Book First Edition
Hardcover. Condition: Very Good. 1. Auflage. Unread, with a mimimum of shelfwear. Immediately dispatched from Germany.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Condition: As New. Unread book in perfect condition.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Book
Condition: As New. Unread book in perfect condition.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Book Print on Demand
Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
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Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
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Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: Books Puddle, New York, NY, U.S.A.
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Condition: New. pp. XI, 461 177 illus., 156 illus. in color. 1 Edition NO-PA16APR2015-KAP.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Book
Condition: As New. Unread book in perfect condition.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
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Condition: As New. Unread book in perfect condition.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: Majestic Books, Hounslow, United Kingdom
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Condition: New. Print on Demand pp. XI, 461 177 illus., 156 illus. in color.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: Brook Bookstore, Milano, MI, Italy
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Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: booksXpress, Bayonne, NJ, U.S.A.
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Hardcover. Condition: new.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: Mispah books, Redhill, SURRE, United Kingdom
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Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Published by Springer-Verlag New York Inc, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: Revaluation Books, Exeter, United Kingdom
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Hardcover. Condition: Brand New. 476 pages. 9.25x6.10x1.34 inches. In Stock.
Published by Springer International Publishing, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: moluna, Greven, Germany
Book Print on Demand
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reviews the state of the art in deep learning approaches to robust disease detection, organ segmentation in medical image computing, and the construction and mining of large-scale radiology databasesParticularly focuses on the application of convo.
Published by Springer International Publishing, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: moluna, Greven, Germany
Book Print on Demand
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reviews the state of the art in deep learning approaches to robust disease detection, organ segmentation in medical image computing, and the construction and mining of large-scale radiology databasesParticularly focuses on the application of convo.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: Books Puddle, New York, NY, U.S.A.
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Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: dsmbooks, Liverpool, United Kingdom
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Published by Springer International Publishing Okt 2019, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation. 476 pp. Englisch.
Published by Springer International Publishing Okt 2020, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation. 476 pp. Englisch.
Published by Springer International Publishing, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Published by Springer International Publishing, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Published by Springer, 2020
ISBN 10: 3030139719ISBN 13: 9783030139711
Seller: California Books, Miami, FL, U.S.A.
Book
Condition: New.
Published by Springer, 2019
ISBN 10: 3030139689ISBN 13: 9783030139681
Seller: California Books, Miami, FL, U.S.A.
Book
Condition: New.