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| Artikel-Nr.: 5667A-9783030139711 Herst.-Nr.: 9783030139711 EAN/GTIN: 9783030139711 |
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![](/p.gif) | 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. Weitere Informationen: ![](/p.gif) | ![](/p.gif) | Author: | Le Lu; Xiaosong Wang; Gustavo Carneiro; Lin Yang | Verlag: | Springer International Publishing | Sprache: | eng |
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![](/p.gif) | Weitere Suchbegriffe: Datenbanken (Fachbücher), Datenbankenbücher, bücher zu anwendungs-software - englischsprachig, Deep Learning, Convolutional Neural Networks, Medical Image Analytics, Computer-Aided Diagnosis, Hospital-Scale Imaging Data Process, Disease Detection, Organ Segmentation, Medical Image Computing, Radiology Database Construction and Mining, Object and Landmark Detection, 2D and 3D Medical Imaging |
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