Overview

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.

Full Product Details

Author:   Yury V. Kistenev ,  Alexey V. Borisov ,  Denis A. Vrazhnov Sr.
Publisher:   SPIE Press
Imprint:   SPIE Press
Weight:   0.333kg
ISBN:  

9781510645349

ISBN 10:   1510645349
Pages:   252
Publication Date:   30 September 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

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

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