Buch, Englisch, 252 Seiten, Paperback
Reihe: Press Monographs
Buch, Englisch, 252 Seiten, Paperback
Reihe: Press Monographs
ISBN: 978-1-5106-4534-9
Verlag: SPIE Press
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
Weitere Infos & Material
- Preface
- 1 Fundamental Concepts Related to Laser Molecular Imaging
- Introduction
- 1.1 Molecular Biomarkers
- 1.1.1 Biomarker conception
- 1.1.2 Groups of molecular ""omics"" biomarkers
- 1.1.3 Pattern-recognition approach for metabolic profiling
- 1.1.4 Biological specimens for noninvasive diagnostics
- 1.2 Basics of Laser Molecular Spectroscopy and Imaging
- 1.2.1 Molecule absorption spectra
- 1.2.2 Raman scattering spectra
- 1.2.3 Fluorescence spectra
- 1.2.4 Molecular imaging
- 1.3 Basics of Machine Learning
- Conclusion
- References
- 2 Laser-based Molecular Data-Acquisition Technologies
- Introduction
- 2.1 Data-Acquisition Technologies Suitable for Breath Biopsy
- 2.1.1 The aim of data acquisition by breathomics
- 2.1.2 Nonoptical experimental methods for breathomics
- 2.1.3 Breath air sampling
- 2.1.4 Laser absorption spectroscopy
- 2.1.5 Fluorescence spectroscopy
- 2.2 Data Acquisition Technologies Suitable for Optical Liquid Biopsy
- 2.2.1 Possible optical modes for liquid sample analysis
- 2.2.2 Data acquisition using unprocessed or drying liquid samples
- 2.3 Data Acquisition Technologies Suitable for Optical Tissue Biopsy
- 2.3.1 Experimental methods for nonoptical tissue biopsy
- 2.3.2 Interaction of laser radiation with a tissue
- 2.3.3 Possible experimental laser spectroscopy methods for in vivo tissue optical biopsy
- 2.3.4 Possible experimental laser molecular imaging methods for in vivo tissue optical biopsy
- Conclusion
- References
- 3 Informative Feature Extraction
- Introduction
- 3.1 Feature Selection
- 3.1.1 Univariate methods of feature selection
- 3.1.2 Multivariate methods of feature selection
- 3.2 Feature Extraction
- 3.3 Outliers and Noise Reduction
- 3.3.1 Outlier removal
- 3.3.2 Noise reduction by signal filtration
- Conclusion
- References
- 4 Clusterization and Predictive Model Construction
- Introduction
- 4.1 Unsupervised Learning Methods: Clusterization
- 4.1.1 K-means algorithm
- 4.1.2 Density-based spatial clustering of applications with noise (DBSCAN)
- 4.1.3 Markov clusterization algorithm (MCL)
- 4.2 Predictive Model Construction
- 4.2.1 Linear discriminant analysis (LDA)
- 4.2.2 K-nearest neighbors (KNN)
- 4.2.3 Partial least squared discriminant analysis (PLS-DA)
- 4.2.4 Soft independent modeling of class analogy (SIMCA)
- 4.2.5 Naive Bayes
- 4.2.6 Support vector machine (SVM)
- 4.2.7 Multi-class decision rules based on binary classifiers
- 4.2.8 A random forest
- 4.2.9 Artificial neural networks
- 4.2.10 Extreme learning machine (ELM)
- 4.2.11 Deep learning neural networks
- 4.2.12 Improving prediction models; ensemble learning
- 4.2.13 Predictive model validation
- Conclusion
- References
- 5 Medical Applications
- Introduction
- 5.1 Breath Optical Biopsy by Laser Absorption Spectroscopy and Machine Learning
- 5.1.1 Machine learning pipeline for chemical-based breathomics
- 5.1.2 Machine learning pipeline for ""profiling""-based breathomics
- 5.2 Liquid Optical Biopsy by IR and THz Laser Spectroscopy and Machine Learning
- 5.2.1 Calibration and pre-processing
- 5.2.2 Chemical-based liquid optical biopsy data modeling by machine learning
- 5.2.3 ""Profiling""-based liquid optical biopsy data modeling by machine learning
- 5.3 Tissue Optical Biopsy Using Laser Molecular Imaging and Machine Learning
- 5.3.1 Calibration and pre-processing
- 5.3.2 Tissue optical biopsy data modeling using machine learning
- Conclusion
- References
- Supplemental Materials
- Index