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 is based on Artificial Neural Networks and 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 and Its Applications

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. Deep Learning is particularly useful in the field of Biomedical Engineering, where the ever-expanding amount of data that can be gathered through biomedical and clinical sensing devices necessitates the development of machine learning and AI techniques to process and evaluate the data.

Biomedical and Clinical Sensing Devices

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

Applications of Deep Learning in Biomedical Engineering

The 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
• Translational multimodal imaging analysis

Key Concepts and Techniques

The handbook helps readers understand key concepts in DL applications for biomedical engineering and health care, including:

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

It also provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks.

Disease Diagnosis and Prediction

The handbook covers key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography.