Differential Diagnosis and Management for the Chiropractor: Protocols and Algorithms (5版)

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【少量特別書籍 若無貨可能須等候5-7日以上, 因”客制特別調書不收退貨” 可接受在下單】 <詹姆斯網路書局> Differential Diagnosis and Management for the Chiropractor: Protocols and Algorithms 464-5700 定價 2,500 ISBN 9781284457001 作者/出版社 Souza / Jones and Bartlett 出版年代/版次 2016/5 Differential Diagnosis and Management for the Chiropractor is included in the 2015 edition of the essential collection of Doody’s Core Titles. The Fifth Edition of this best-selling reference is a compendium of evidence-based approaches to the most common presenting complaints. Covering both musculoskeletal and visceral complaints, this text is intended to direct the chiropractor toward an appropriate plan of approach in both diagnostic evaluation and care. Highlighting these approaches are flowcharts (algorithms), relevant historical questioning, and summaries of common conditions related to the presenting complaint. What’s New in the 5th Edition? · Additional disorders added to Selected Causes at the conclusion of chapters · Addition of Likelihood Ratio graphics · Addition of approximately 500 new references · New Appendix: Evidence Based Approach to the Literature · Expanded Appendix: Pharmacology for the Chiropractor includes newer drugs and further explains the classifications of medications mechanisms · Translation into Practice Summary (TIPS) for most of the orthopedic chapters · Updated Algorithms: Cervical spine, Lumbar spine, Shoulder, Knee

Deep Learning for 3D Vision Algorithms and Applications

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【簡介】 3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications. This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing. This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning. 【目錄】