Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications

With Scikit-Learn and PyTorch

1st Edition - August 1, 2022

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  • Authors: Maria Deprez, Emma Robinson
  • Paperback ISBN: 9780128229040

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Description

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

Key Features

  • Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis
  • Shows how to apply a range of commonly used machine learning and deep learning techniques in biomedical problems
  • Develops practical computational skills that are needed to manipulate complex biomedical data sets
  • Shows how to design machine learning experiments that address specific problems related to biomedical data

Readership

Biomedical engineering undergraduates, graduates, researchers. Biomedical science students and researchers; clinical researchers

Table of Contents

  • Part I: Introduction and background
    1. Machine learning for Biomedical applications
    2. Programming background
    3. Mathematical background

    Part II: Machine Learning Methods
    4. Regression
    5. Classification
    6. Ensemble methods
    7. Dimensionality reduction and Manifold learning
    8. Feature extraction and selection
    9. Clustering 10. Neural networks

    Part III: Deep Learning
    11. Building blocks of deep neural networks
    12. Common architectures
    13. Generative models
    14. The challenges of working with biomedical data

    Part IV: Tricks of the trade

Product details

  • No. of pages: 326
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: August 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128229040

About the Authors

Maria Deprez

Dr Maria Deprez is a Lecturer in Medical Imaging in the Department of Perinatal Imaging & Health at the School of Biomedical Engineering & Imaging Sciences. Her Research interests are in motion correction and reconstruction of fetal and placental MRI, Spatio-temporal models of developing brain, segmentation, registration, atlases, machine learning, and deep learning

Affiliations and Expertise

Lecturer in Medical Imaging, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, UK

Emma Robinson

Dr Robinson's research focuses on the development of computational methods for brain imaging analysis, and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of  of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex “ (Glasser et al, Nature 2016), and has featured as a central tenet in the HCP’s paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples.

Affiliations and Expertise

King’s College London, UK

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