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Titlebook: Handbook of Artificial Intelligence in Healthcare; Vol. 1 - Advances an Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2022 The Editor(s

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书目名称Handbook of Artificial Intelligence in Healthcare
副标题Vol. 1 - Advances an
编辑Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain
视频videohttp://file.papertrans.cn/421/420835/420835.mp4
概述Provides a comprehensive overview of Artificial Intelligence in Healthcare.Focuses on Recent Advances in Healthcare Data Analytics.Is written by experts in the field
丛书名称Intelligent Systems Reference Library
图书封面Titlebook: Handbook of Artificial Intelligence in Healthcare; Vol. 1 - Advances an Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2022 The Editor(s
描述.This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. .The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans..In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector.   .
出版日期Book 2022
关键词Artificial Intelligence in Healthcare; Technology for Ageing Populations; Healthcare; Artificial Intell
版次1
doihttps://doi.org/10.1007/978-3-030-79161-2
isbn_softcover978-3-030-79163-6
isbn_ebook978-3-030-79161-2Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-981-13-1177-2rders that make it difficult the communication with other people. Their natural language presents different degrees of alteration, reaching in some cases the impossibility of speaking. This chapter presents an approach to model the patient’s behavior by processing recordings during the therapy. Vide
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https://doi.org/10.1007/978-3-031-61191-9sis is as evident in medicine as it is elsewhere. Numerous artificial intelligence techniques been applied to different medical problems with the aim of automating time-consuming, and often subjective, manual tasks implemented by practitioners in diverse specialties. This chapter focuses on several
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The Audience as Myth and Realitynical background and setting for the opportunistic development of cancer imaging biomarkers from such routine imaging. The chapter is aimed at the clinicians with a data science interest as well as data scientists with a clinical interest, and touches on computational approaches on radiological data
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https://doi.org/10.1057/9781137478818el of multiple input and multiple output structure to detect LST-type polyp with high accuracy, which is based on U-Net architecture for the segmentation. Not only the original endoscope image but also depth map is also used to the original CNN structure of 2 inputs and 4 outputs. Here, proposed met
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https://doi.org/10.1057/9780230276499 a major cause of early postoperative recurrence of HCC. Predicting MVI before surgery can help doctors develop treatment plans. However, the diagnosis of MVI depends on postoperative pathological verification, which is difficult to predict before surgery. In recent years, more researchers have used
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