Medical Applications of Laser Molecular Imaging and Machine Learning

Häftad, Engelska, 2021

842 kr

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Beskrivning

This book focuses on machine-learning medical applications based on molecular spectroscopy and molecular imaging data. Written with specialists in biomedical optics, laser spectroscopy, bioengineering, and medical engineering in mind, the chapters cover topics such as biomarker conception, molecular laser imaging, and artificial intelligence; laser-based molecular-data-acquisition technologies; feature selection and extraction methods; unsupervised and supervised approaches; and in vivo non-invasive diagnostics using laser molecular spectroscopy and imaging combined with machine learning. Sample datasets and Python modules are provided as supplemental materials for the most useful algorithms.

Produktinformation

Utgivningsdatum:2021-09-30
Vikt:333 g
Format:Häftad
Språk:Engelska
Serie:Press Monographs
Antal sidor:252
Förlag:SPIE Press
ISBN:9781510645349

Utforska kategorier

Innehållsförteckning

Preface1 Fundamental Concepts Related to Laser Molecular ImagingIntroduction1.1 Molecular Biomarkers1.1.1 Biomarker conception1.1.2 Groups of molecular ""omics"" biomarkers1.1.3 Pattern-recognition approach for metabolic profiling1.1.4 Biological specimens for noninvasive diagnostics1.2 Basics of Laser Molecular Spectroscopy and Imaging1.2.1 Molecule absorption spectra1.2.2 Raman scattering spectra1.2.3 Fluorescence spectra1.2.4 Molecular imaging1.3 Basics of Machine LearningConclusionReferences2 Laser-based Molecular Data-Acquisition TechnologiesIntroduction2.1 Data-Acquisition Technologies Suitable for Breath Biopsy2.1.1 The aim of data acquisition by breathomics2.1.2 Nonoptical experimental methods for breathomics2.1.3 Breath air sampling2.1.4 Laser absorption spectroscopy2.1.5 Fluorescence spectroscopy2.2 Data Acquisition Technologies Suitable for Optical Liquid Biopsy2.2.1 Possible optical modes for liquid sample analysis2.2.2 Data acquisition using unprocessed or drying liquid samples2.3 Data Acquisition Technologies Suitable for Optical Tissue Biopsy2.3.1 Experimental methods for nonoptical tissue biopsy2.3.2 Interaction of laser radiation with a tissue2.3.3 Possible experimental laser spectroscopy methods for in vivo tissue optical biopsy2.3.4 Possible experimental laser molecular imaging methods for in vivo tissue optical biopsyConclusionReferences3 Informative Feature ExtractionIntroduction3.1 Feature Selection3.1.1 Univariate methods of feature selection3.1.2 Multivariate methods of feature selection3.2 Feature Extraction3.3 Outliers and Noise Reduction3.3.1 Outlier removal3.3.2 Noise reduction by signal filtrationConclusionReferences4 Clusterization and Predictive Model ConstructionIntroduction4.1 Unsupervised Learning Methods: Clusterization4.1.1 K-means algorithm4.1.2 Density-based spatial clustering of applications with noise (DBSCAN)4.1.3 Markov clusterization algorithm (MCL)4.2 Predictive Model Construction4.2.1 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 Bayes4.2.6 Support vector machine (SVM)4.2.7 Multi-class decision rules based on binary classifiers4.2.8 A random forest4.2.9 Artificial neural networks4.2.10 Extreme learning machine (ELM)4.2.11 Deep learning neural networks4.2.12 Improving prediction models; ensemble learning4.2.13 Predictive model validationConclusionReferences5 Medical ApplicationsIntroduction5.1 Breath Optical Biopsy by Laser Absorption Spectroscopy and Machine Learning5.1.1 Machine learning pipeline for chemical-based breathomics5.1.2 Machine learning pipeline for ""profiling""-based breathomics5.2 Liquid Optical Biopsy by IR and THz Laser Spectroscopy and Machine Learning5.2.1 Calibration and pre-processing5.2.2 Chemical-based liquid optical biopsy data modeling by machine learning5.2.3 ""Profiling""-based liquid optical biopsy data modeling by machine learning5.3 Tissue Optical Biopsy Using Laser Molecular Imaging and Machine Learning5.3.1 Calibration and pre-processing5.3.2 Tissue optical biopsy data modeling using machine learningConclusionReferencesSupplemental MaterialsIndex