Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications: Scikit-Learn and PyTorch introduce the most commonly used machine learning techniques in a biomedical scenario.

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Summary Machine Learning in Biomedical Engineering

It avoids a theoretical approach in favor of a practical and participatory approach to learning, with principles offered in short descriptions and easy examples based on biomedical data.

Each chapter includes interactive Python notebooks to supplement the text and improve comprehension. Uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the area, essential machine learning methods, profound learning principles with Keras examples, and much more are covered in various sections.

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Undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences, and physicians will benefit from this interactive and straightforward introduction to machine learning and data analysis skills.

Release Machine Learning for Biomedical Applications

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About Author: Machine Learning in Biomedical Applications

Dr. Maria Deprez is a Lecturer in Medical Imaging at the School of Biomedical Engineering & Imaging Sciences’ Department of Perinatal Imaging & Health. Her research interests include fetal and placental MRI motion correction and reconstruction, spatiotemporal models of the developing brain, segmentation, registration, atlases, machine learning, and deep learning.

Dr. Robinson’s research focuses on the development of computational approaches for brain imaging analysis and a variety of image processing and machine learning topics. Most notably, her cortical surface registration software (Multimodal Surface Matching, MSM).

Instrumental in the development of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex” (Glasser et al., Nature 2016) and has been a central tenet in the HCP’s “Multi-modal parcellation of the Human Cortex” (Glasser et al., Nature 2016).. 

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Tags : Machine Learning for Biomedical Applications

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Conclusion: Deep Learning for Biomedical Applications