Mining Biomedical Text, Images and Visual Features for Information Retrieval
Language: English
Published by Elsevier Science Nov 2024, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 287.58
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Add to basketItem description from seller
Neuware - Mining Biomedical Text, Images and Visual Features for Information Retrieval provides broad coverage of the concepts, themes, and instrumentalities of the important, evolving area of biomedical text, images, and visual features towards information retrieval. The book aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research. Topics covered include Internet of Things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications. This is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work.
Seller Inventory # 9780443154522
- Title
- Mining Biomedical Text, Images and Visual Features for Information Retrieval
- Author
- Jake Y Chen
- Publisher
- Elsevier Science Nov 2024
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 044315452X
- ISBN 13
- 9780443154522
- Item weight
- 1,365 grams
- Dimensions
- 235x191x31 mm
- Describes many biomedical imaging techniques to detect diseases at the cellular level i.e., image segmentation, classification, or image indexing using a variety of computational intelligence and image processing approaches
- Discusses how data mining techniques can be used for noise diminution and filtering MRI, EEG, MEG, fMRI, fNIRS, and PET Images
- Presents text mining techniques used for clinical documents in the areas of medicine and Biomedical NLP Systems
"Synopsis" may belong to another edition of this title.
About the Author
Subhendu Kumar Pani received his Ph.D. from Utkal University Odisha, India. He has more than 16 years of teaching and research experience. His research interests include data mining, big data analysis, web data analytics, fuzzy decision making and computational intelligence. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, CSI.
Professor dos Santos is creator and developer of innovative healthcare solutions for diagnosis and treatment using Artificial Intelligence. Applications in digital epidemiology, neuroscience, diagnostic imaging, diagnosis by signs, diagnosis by laboratory tests, health informatics and bioinformatics. Founder of the Ada Lovelace Association. Leader of the Research Group on Biomedical Computing at UFPE. Enthusiast of social entrepreneurship and innovation in health.
Before joining UAB, Dr. Chen was the founding director of the Indiana Center for Systems Biology and Personalized Medicine at Indiana University and a tenured faculty member at Indiana University School of Informatics and Purdue University Computer Science Department. Dr. Chen has over 20 years of research and development experience in biological data mining, systems biology, and translational informatics in both Academia and the industry. He has over 150 peer-reviewed publications and presented worldwide on topics related to biocomputing, bioinformatics, and data sciences in life sciences. He was elected as the President-elect of the Midsouth Computational Biology and Bioinformatics Society (MCBIOS) in 2019. He also serves on the editorial boards of BMC Bioinformatics, Journal of American Medical Informatics Association (JAMIA), and Personalized Medicine.
"About the title" may belong to another edition of this title.
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