Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch
Author(s): Deprez (Author), Robinson (Author)
Publisher: Academic Press
Publication Date: 13 Sept. 2023
Edition: 1st
Language: English
Print length: 304 pages
ISBN-10: 0128229047
ISBN-13: 9780128229040
Book 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.
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
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An easy to understand, interactive learning resources that presents machine and deep learning methods as applied to biomedical engineering
From the Back Cover
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 solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding.
The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical Python coding skills, and mathematical tool that underpin the field; core machine learning methods; Deep learning concepts with examples in Keras. ; tricks of the trade where guidance is given on best practice for data preparation and experimental design to aid the successful application of machine learning methods to real world biomedical data.
This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, biomedical science, and clinicians.
About the Author
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
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.
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