Machine Learning Applications in Medicine and Biology

Erscheinungsjahr 2025
ISBN: 978-3-031-51895-9
Verlag: Springer

Buch, Englisch, 168 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g

This book combines selected papers from the 2022 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) held at Temple University. The symposium presents multidisciplinary research in the life sciences. Topics covered include:

• Signal and image analysis (EEG, ECG, MRI)
• Machine learning
• Data mining and classification
• Big data resources

Applications of particular interest at the 2022 symposium included digital pathology, computational biology, and quantum computing. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers in signal processing, medicine, and biology.

Zielgruppe

Research

Autoren/Hrsg.

Ahmed, Ammar

Picone, Joseph

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Naturwissenschaften Chemie Analytische Chemie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

Introduction.- Signal and Image Analysis (EEG, ECG, MRI).- Machine Learning.- Data Mining and Classification.- Big Data.- Index.

Über Autor(innen)

Ammar Ahmed, Ph.D., received his Ph.D. degree from Temple University, USA, in 2021. He is a radar signal processing engineer at Aptiv, Agoura Hills, CA, USA. From 2011 to 2016, Dr. Ahmed served as an electrical engineer for the National Tokamak Fusion Program, where he was responsible for developing an embedded system design of spherical tokamaks. He also spent the summer of 2020 (May-August) as an intern at Qualcomm Technologies, Inc., in San Diego, CA. His research interests are signal processing, data analysis, optimization, and radar systems. 

Joseph Picone, Ph.D., received his Ph.D. in Electrical Engineering in 1983 from the Illinois Institute of Technology. He is a professor in the Department of Electrical and Computer Engineering at Temple University. He has spent significant portions of his career in academia (MS State), research (Texas Instruments, AT&T), and the government (NSA). His primary research interests currently are applications of machine learning in the health sciences. Dr. Picone’s research funding sources over the years have included NSF, NIH, DoD, and DARPA as well as the private sector. He has also been involved in several startup companies in healthcare. For over 40 years, his research groups have been known for producing many innovative open-source materials for education and research, including the first state of the art public domain speech recognition system and the TUH EEG Corpus, which has over 8,000 subscribers (see www.isip.piconepress.com). Dr. Picone is a Senior Member of the IEEE, has published extensively in many signal processing-related fields, and holds several patents in human language technology.