Deep Learning in Biomedical Engineering: Techniques and Applications

Deep Learning (DL) is a method of machine learning that uses multiple layers to extract high-level features from large amounts of raw data. It applies levels of learning to transform input data into more abstract and composite information. The Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides a comprehensive overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering.

Deep Learning Applications in Biomedical Engineering

Deep Learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. 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. 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
• Atomic Force Microscopy

Real-World Applications of Deep Learning in Biomedical Engineering

This handbook 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
• Translational multimodal imaging analysis

Key Concepts and Techniques in Deep Learning Applications

Readers will understand key concepts in DL applications for biomedical engineering and health care, including:

• Manifold learning
• Classification
• Clustering
• Regression in neuroimaging data analysis

Additionally, readers will learn key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks.

Author and Publication Details

This comprehensive handbook is authored by Valentina Emilia Balas and published by Elsevier Science on November 12, 2020. The language of the book is English, and the ISBN is 9780128230145 (hardcover) and 9780128230473 (e-book).