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Machine Learning for Biomedical Applications
With Scikit-Learn and PyTorch
- 1st Edition - September 7, 2023
- Authors: Maria Deprez, Emma C. Robinson
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 9 0 4 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 9 0 5 - 7
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theore… Read more
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Request a sales quoteMachine 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.
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.
- 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 to biomedical problems.
- Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets.
- Shows how to design machine learning experiments that address specific problems related to biomedical data
Biomedical engineering undergraduates, graduates, researchers, Biomedical science students and researchers, clinical researchers
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Machine learning in today's world
- The aims of this book
- Learning objectives
- How to use this book
- Prerequisites
- Acknowledgments
- References
- Chapter 1: Programming in Python
- Abstract
- 1.1. Getting started
- 1.2. Variable types and operators
- 1.3. Indexing and slicing
- 1.4. Control flow
- 1.5. Conditional (if) statements
- 1.6. For statements
- 1.7. Functions
- 1.8. Modules, packages, and classes
- 1.9. NumPy
- 1.10. A MATLAB to Python cheatsheet
- 1.11. Pandas
- 1.12. Matplotlib
- References
- Chapter 2: Machine learning basics
- Abstract
- 2.1. What is machine learning?
- 2.2. Starting with scikit-learn
- 2.3. Training machine learning models
- Chapter 3: Regression
- Abstract
- 3.1. Regression basics
- 3.2. Penalized regression
- 3.3. Nonlinear regression
- References
- Chapter 4: Classification
- Abstract
- 4.1. Classification basics
- 4.2. Support vector classifier
- References
- Chapter 5: Dimensionality reduction
- Abstract
- 5.1. The curse of dimensionality
- 5.2. Low-dimensional embedding: a physical motivation
- 5.3. Linear transforms
- 5.4. Principal component analysis
- 5.5. Independent component analysis
- 5.6. Manifold learning
- 5.7. Laplacian eigenmaps
- References
- Chapter 6: Clustering
- Abstract
- 6.1. K-means clustering
- 6.2. Gaussian mixture model
- 6.3. Spectral clustering
- References
- Chapter 7: Decision trees and ensemble learning
- Abstract
- 7.1. Decision trees
- 7.2. Ensemble learning
- References
- Chapter 8: Feature extraction and selection
- Abstract
- 8.1. Feature extraction
- 8.2. Feature selection
- Chapter 9: Deep learning basics
- Abstract
- 9.1. An artificial neuron
- 9.2. Starting with Pytorch
- 9.3. Single-layer perceptron
- References
- Chapter 10: Fully connected neural networks
- Abstract
- 10.1. Fully connected network architecture
- 10.2. Training neural networks
- 10.3. Predicting age from brain connectivity using deep learning
- 10.4. Conclusions
- References
- Chapter 11: Convolutional neural networks
- Abstract
- 11.1. Why convolution?
- 11.2. Building blocks of convolutional neural networks
- 11.3. Predicting prematurity from neonatal brain MRI
- 11.4. CNN segmentation for medical images
- 11.5. Conclusion
- References
- References
- References
- Index
- No. of pages: 326
- Language: English
- Edition: 1
- Published: September 7, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780128229040
- eBook ISBN: 9780128229057
MD
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
Senior Lecturer in Medical Imaging, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, UKER
Emma C. 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
Senior Lecturer, King’s College London, UKRead Machine Learning for Biomedical Applications on ScienceDirect