Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Heung-Il Suk
Publisher: Springer Nature
Total Pages: 711
Release: 2019-10-09
Genre: Computers
ISBN: 3030326926

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

An Update on Anxiety Disorders

An Update on Anxiety Disorders
Author: Marwa Azab
Publisher: Springer Nature
Total Pages: 236
Release: 2022-11-14
Genre: Psychology
ISBN: 3031193628

This book aims to synthesize recent theoretical and experimental findings from psychology, neuroscience, epigenetics and genetics to understand anxiety disorders and their etiology and treatments. Each anxiety disorder is discussed from cognitive, behavioral and biological perspectives. The book evaluates talk therapies, mindfulness-based interventions, brain stimulation, biofeedback and neurofeedback treatments. Chapters consider a biologically-informed framework for the understanding of anxiety disorders. In line with current thinking, the book integrates many levels of information (from genomics and circuits to behavior and self-report) to understand normal and abnormal human behaviors. Synthesizing recent research on anxiety disorders according to their categorization in the DSM5, this book will bring psychology students, researchers, psychiatrists and psychologists up to date.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author: Guorong Wu
Publisher: Academic Press
Total Pages: 514
Release: 2016-08-11
Genre: Computers
ISBN: 0128041145

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques

Advances in Resting-State Functional MRI

Advances in Resting-State Functional MRI
Author: Jean Chen
Publisher: Elsevier
Total Pages: 382
Release: 2023-07-03
Genre: Psychology
ISBN: 0323985459

Advances in Resting-State Functional MRI: Methods, Interpretation, and Applications gives readers with basic neuroimaging experience an up-to-date and in-depth understanding of the methods, opportunities, and challenges in rs-fMRI. The book covers current knowledge gaps in rs-fMRI, including "what are biologically plausible brain networks," "how to tell what part is noise," "how to perform quality assurance on the data," "what are the spatial and temporal limits of our ability to resolve FC," and "how to best identify network features related to individual differences or disease state". This book is an ideal reference for neuroscientists, computational neuroscientists, psychologists, biomedical engineers, physicists and medical physicists. Both new and more advanced researchers alike will be able to discover new information distilled from the past decade of research to become well-versed in rs-fMRI-related topics. - Presents the first book to explain the latest methods, opportunities and challenges of Resting-state Functional MRI - Edited and authored by leading researchers in fMRI - Includes neuroscientific and clinical applications

fMRI

fMRI
Author: Peter A. Bandettini
Publisher: MIT Press
Total Pages: 282
Release: 2020-02-25
Genre: Science
ISBN: 0262538032

An accessible introduction to the history, fundamental concepts, challenges, and controversies of the fMRI by one of the pioneers in the field. The discovery of functional MRI (fMRI) methodology in 1991 was a breakthrough in neuroscience research. This non-invasive, relatively high-speed, and high sensitivity method of mapping human brain activity enabled observation of subtle localized changes in blood flow associated with brain activity. Thousands of scientists around the world have not only embraced fMRI as a new and powerful method that complemented their ongoing studies but have also gone on to redirect their research around this revolutionary technique. This volume in the MIT Press Essential Knowledge series offers an accessible introduction to the history, fundamental concepts, challenges, and controversies of fMRI, written by one of the pioneers in the field. Peter Bandettini covers the essentials of fMRI, providing insight and perspective from his nearly three decades of research. He describes other brain imaging and assessment methods; the sources of fMRI contrasts; the basic methodology, from hardware to pulse sequences; brain activation experiment design strategies; and data and image processing. A unique, standalone chapter addresses major controversies in the field, outlining twenty-six challenges that have helped shape fMRI research. Finally, Bandettini lays out the four essential pillars of fMRI: technology, methodology, interpretation, and applications. The book can serve as a guide for the curious nonexpert and a reference for both veteran and novice fMRI scientists.

Deep Learning Methods and Applications in Brain Imaging for the Diagnosis of Neurological and Psychiatric Disorders

Deep Learning Methods and Applications in Brain Imaging for the Diagnosis of Neurological and Psychiatric Disorders
Author: Hao Zhang
Publisher: Frontiers Media SA
Total Pages: 151
Release: 2024-10-14
Genre: Science
ISBN: 2832555500

Brain imaging has been successfully used to generate image-based biomarkers for various neurological and psychiatric disorders, such as Alzheimer’s and related dementias, Parkinson’s disease, stroke, traumatic brain injury, brain tumors, depression, schizophrenia, etc. However, accurate brain image-based diagnosis at the individual level remains elusive, and this applies to the diagnosis of neuropathological diseases as well as clinical syndromes. In recent years, deep learning techniques, due to their ability to learn complex patterns from large amounts of data, have had remarkable success in various fields, such as computer vision and natural language processing. Applying deep learning methods to brain imaging-assisted diagnosis, while promising, is facing challenges such as insufficiently labeled data, difficulty in interpreting diagnosis results, variations in data acquisition in multi-site projects, integration of multimodal data, clinical heterogeneity, etc. The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.