Details
Explainable Machine Learning in Medicine
Synthesis Lectures on Engineering, Science, and Technology
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85,59 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 26.11.2023 |
| ISBN/EAN: | 9783031448775 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
This book covers a variety of advanced communications technologies that can be used to analyze medical data and can be used to diagnose diseases in clinic centers. The book is a primer of methods for medicine, providing an overview of explainable artificial intelligence (AI) techniques that can be applied in different medical challenges. The authors discuss how to select and apply the proper technology depending on the provided data and the analysis desired. Because a variety of data can be used in the medical field, the book explains how to deal with challenges connected with each type. A number of scenarios are introduced that can happen in real-time environments, with each pared with a type of machine learning that can be used to solve it.<p></p>
1 Introduction.- 2 Medical Tabular Data.- 3 Natural Language Processing for Medical Data Analysis.- 4 Computer Vision for Medical Data Analysis.- 5 Time Series Data Used for Diseases Recognition and Anomaly Detection.- 6 Summary.<p></p>
<div>Dr. Karol Przystalski obtained a PhD degree in Computer Science in 2017 at the Jagiellonian University in Cracow, Poland. He use to be the CTO and founder of Codete, an Exadel company. He is working with Fortune 500 companies on data science projects. He has built a research lab for machine learning methods and big data solutions at Codete. He gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English. He is a lecturer at the Jagiellonian University in Cracow since 2010. He has been invited as a reviewer in Expert Systems with Applications journal. His areas of research interest are medical imaging analysis, artificial intelligence, machine learning, deep learning, machine learning security, pattern recognition, and image processing.</div><div> <br> Dr. Rohit Thanki is a senior member of IEEE and researcher with more than 10 years of research experience in computer vision, artificial intelligence, medical image analysis & security, and biometrics, including more than 4 years of academic experience in various engineering institutions in India. He was worked as a head of research & development, Prognica Labs Tech FZCO, Dubai, UAE. Also, He was associated with Ennoventure Technologies Private Limited, Bengaluru, India. He earned my bachelor's in electronics & communication, a master's in communication engineering, and a doctorate in electronics & communication specializing in digital image processing and biometric security. His areas of research interest are medical image analysis, artificial intelligence, machine learning, deep learning, digital watermarking, biometric security, compressive sensing, and signal processing. He has over 40 publications to his credit and has published in reputed journals with high impact factors and international conferences indexing in Web of Science and Scopus. Also, He is an authored and contributed more than 15 books with respected publishers, i.e., Springer, CRC Press, Elsevier, De Gruyter, and IGI Global. In addition, He has been invited as a reviewer in various reputed journals such as IEEE Transactions on Audio, Speech and Natural Language Processing, ACM Transactions on Multimedia Computing, Communications and Applications, IEEE Consumer Electronics Magazine, IEEE Access, IEEE Journal of Biomedical and Health Informatics, Signal Processing: Image Communication, Pattern Recognition, Computers, and Electrical Engineering, Informatics in Medicine, Journal of Ambient Intelligence and Humanized Computing, IET Biometrics, and IET Image Processing.<p></p></div>
This book covers a variety of advanced communications technologies that can be used to analyze medical data and can be used to diagnose diseases in clinic centers. The book is a primer of methods for medicine, providing an overview of explainable artificial intelligence (AI) techniques that can be applied in different medical challenges. The authors discuss how to select and apply the proper technology depending on the provided data and the analysis desired. Because a variety of data can be used in the medical field, the book explains how to deal with challenges connected with each type. A number of scenarios are introduced that can happen in real-time environments, with each pared with a type of machine learning that can be used to solve it.<div><br></div><div><ul><li>Provides a detailed primer on explainable artificial intelligence (AI) and machine learning (ML) methods that can be used in medical cases</li><li>Presents how explainable AI can aid in choosing ML algorithms that give better performance in medical applications<br></li><li>Covers various kinds of medical data used to diagnose diseases in clinic centers and what technologies are best suited to analyze it<br></li></ul></div>
Provides a primer on explainable artificial intelligence and machine learning methods that can be used in medical cases Presents how explainable AI aids in choosing ML algorithms that give better performance in medical applications Covers medical data used to diagnose diseases in clinic centers and what technologies are best suited to analyze it
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