Machine Learning for Biomedical Applications

Name: Machine Learning for Biomedical Applications
Brand: Academic Press
SKU: 9780128229040
Price: 6500.0 INR
Availability: InStock

by Maria Deprez

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ISBN13: 9780128229040
Binding: Paperback
Subject: N/A
Publisher: Academic Press
Publisher Imprint: Academic Press
Publication Date: September 13, 2023
Pages: N/A
Original Price: GBP 57.95
Language: English
Edition: N/A
Item Weight: 617 grams
BISAC Subject(s): Artificial Intelligence / General, Artificial Intelligence / Computer Vision & Pattern Recognition, and Data Science / Neural Networks

About The Book

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.

About The Author

Deprez, Maria: - 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

Robinson, Emma C.: - 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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