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Books > Medicine > Nursing & ancillary services > Biomedical engineering
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Handbook of Deep Learning in Biomedical Engineering - Techniques and Applications (Paperback)
Loot Price: R3,609
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Handbook of Deep Learning in Biomedical Engineering - Techniques and Applications (Paperback)
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Deep Learning (DL) is a method of machine learning, running over
Artificial Neural Networks, that uses multiple layers to extract
high-level features from large amounts of raw data. Deep Learning
methods apply levels of learning to transform input data into more
abstract and composite information. Handbook for Deep Learning in
Biomedical Engineering: Techniques and Applications gives readers a
complete overview of the essential concepts of Deep Learning and
its applications in the field of Biomedical Engineering. Deep
learning has been rapidly developed in recent years, in terms of
both methodological constructs and practical applications. Deep
Learning provides computational models of multiple processing
layers to learn and represent data with higher levels of
abstraction. It is able to implicitly capture intricate structures
of large-scale data and is ideally suited to many of the hardware
architectures that are currently available. The ever-expanding
amount of data that can be gathered through biomedical and clinical
information sensing devices necessitates the development of machine
learning and AI techniques such as Deep Learning and Convolutional
Neural Networks to process and evaluate the data. Some examples of
biomedical and clinical sensing devices that use Deep Learning
include: Computed Tomography (CT), Magnetic Resonance Imaging
(MRI), Ultrasound, Single Photon Emission Computed Tomography
(SPECT), Positron Emission Tomography (PET), Magnetic Particle
Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic
Tomography, Electron Tomography, and Atomic Force Microscopy.
Handbook for Deep Learning in Biomedical Engineering: Techniques
and Applications provides the most complete coverage of Deep
Learning applications in biomedical engineering available,
including detailed real-world applications in areas such as
computational neuroscience, neuroimaging, data fusion, medical
image processing, neurological disorder diagnosis for diseases such
as Alzheimer's, ADHD, and ASD, tumor prediction, as well as
translational multimodal imaging analysis.
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