Machine Learning for Biomedical Applications
With Scikit-Learn and PyTorch
- 1st Edition - September 7, 2023
- Latest edition
- Authors: Maria Deprez, Emma C. Robinson
- Language: English
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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Description
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.
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
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 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
Readership
Readership
Biomedical engineering undergraduates, graduates, researchers, Biomedical science students and researchers, clinical researchers
Table of contents
Table of contents
1. Programming in Python
2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks
2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks
Product details
Product details
- Edition: 1
- Latest edition
- Published: September 7, 2023
- Language: English
About the authors
About the authors
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, UKView book on ScienceDirect
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