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OverviewMachine 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. Full Product DetailsAuthor: Maria Deprez (Senior Lecturer in Medical Imaging, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, UK) , Emma C. Robinson (Senior Lecturer, King’s College London, UK)Publisher: Elsevier Science Publishing Co Inc Imprint: Academic Press Inc Weight: 1.000kg ISBN: 9780128229040ISBN 10: 0128229047 Pages: 304 Publication Date: 13 September 2023 Audience: College/higher education , Tertiary & Higher Education Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of Contents1. 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 NetworksReviewsAuthor InformationDr 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. Tab Content 6Author Website:Countries AvailableAll regions |
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