Signal Processing in Medicine and Biology
Applications of Deep Learning to the Health Sciences
Buch in deiner Nähe kaufen
Beschreibung
Signal Processing in Medicine and Biology: Applications of Deep Learning to the Health Sciences presents expanded versions of selected papers from the 2023 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) at Temple University. The symposium presents multidisciplinary research across a wide range of topics in the life sciences. The Neural Engineering Data Consortium hosts the symposium to promote machine learning and big data applications in bioengineering.
Topics covered include:
- Signal and image analysis (e.g., EEG, ECG, MRI);
- Machine learning, data mining, and classification;
- Big data resources and applications;
- Applications of quantum computing;
- Digital pathology;
- Computational biology;
- Genomics, genetics, proteomics.
Applications of particular interest at the 2023 symposium included digital pathology, computational biology, genomics, genetics, and proteomics. The book features tutorials and examples of successful applications that will appeal to many professionals and researchers in signal processing, medicine, and biology. For students and professionals new to the field, the book offers an easy-to-understand introduction to various bioengineering topics. For professionals active in the field, it provides essential algorithmic details on valuable benchmarks for the technology.
- Presents an interdisciplinary look at research trends in signal processing and biomedicine;
- Promotes collaboration between practitioners and researchers;
- Includes tutorials and examples of successful applications.
Signal Processing in Medicine and Biology: Applications of Deep Learning to the Health Sciences presents expanded versions of selected papers from the 2023 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) at Temple University. The symposium presents multidisciplinary research across a wide range of topics in the life sciences. The Neural Engineering Data Consortium hosts the symposium to promote machine learning and big data applications in bioengineering.
Topics covered include:
- Signal and image analysis (e.g., EEG, ECG, MRI);
- Machine learning, data mining, and classification;
- Big data resources and applications;
- Applications of quantum computing;
- Digital pathology;
- Computational biology;
- Genomics, genetics, proteomics.
Applications of particular interest at the 2023 symposium included digital pathology, computational biology, genomics, genetics, and proteomics. The book features tutorials and examples of successful applications that will appeal to many professionals and researchers in signal processing, medicine, and biology. For students and professionals new to the field, the book offers an easy-to-understand introduction to various bioengineering topics. For professionals active in the field, it provides essential algorithmic details on valuable benchmarks for the technology.
Presents an interdisciplinary look at research trends in signal processing and biomedicine Promotes collaboration between practitioners and researchers Includes tutorials and examples of successful applications
Autor*in
Ammar Ahmed
Themen in »Signal Processing in Medicine and Biology«
Artificial Intelligence (AI) Deep Learning (DL) Digital Electroencephalography (EEG) Digital Pathology Explainable AI (XAI) Functional Near Infrared Spectroscopy (fNIRS) Machine Learning (ML) Neurodegenerative Diseases Neurophysiological Signals Seismocardiographic Signals