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Artificial Intelligence --- Machine Learning --- Deep Learning --- Diagnostic Imaging --- Image Processing, Computer-Assisted --- Image Interpretation, Computer-Assisted.

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Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more.

E-books --- Medical imaging equipment industry. --- Diagnostic imaging. --- Medical informatics. --- Computer networks --- Diagnostic Imaging --- Deep Learning --- Image Interpretation, Computer-Assisted --- Image Processing, Computer-Assisted --- Design. --- Diagnostic Imaging. --- Deep Learning. --- Image Interpretation, Computer-Assisted. --- Image Processing, Computer-Assisted.

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Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores the implementation of novel deep learning and CNN methodologies and their impact studies that have been tested on different medical case studies Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important Includes novel methodologies, datasets, design and simulation examples.

Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Artificial intelligence --- Biomedical engineering. --- Deep learning (Machine learning) --- Diagnostic imaging --- Electrophysiology. --- Medical applications. --- Data processing. --- Animal electricity --- Bioelectricity --- Electricity, Animal --- Electrobiology --- Neurology --- Physiology --- Electricity --- Clinical imaging --- Imaging, Diagnostic --- Medical diagnostic imaging --- Medical imaging --- Noninvasive medical imaging --- Diagnosis, Noninvasive --- Imaging systems in medicine --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Physiological effect --- Data processing --- Deep Learning --- Electrophysiology --- Image Processing, Computer-Assisted --- Image Interpretation, Computer-Assisted --- Deep Learning. --- Image Processing, Computer-Assisted. --- Image Interpretation, Computer-Assisted.