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.
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
Author(s): Maria Deprez; Emma C. Robinson
Publisher: Elsevier Science & Technology
Year: 2023
Language: English
Pages: 306
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
