Deep Learning And Data Labeling For Medical Applications
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Deep Learning and Data Labeling for Medical Applications
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Machine Learning and Deep Learning Techniques for Medical Science
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).
Deep Learning and Medical Applications
Over the past 40 years, diagnostic medical imaging has undergone remarkable advancements in CT, MRI, and ultrasound technology. Today, the field is experiencing a major paradigm shift, thanks to significant and rapid progress in deep learning techniques. As a result, numerous innovative AI-based programs have been developed to improve image quality and enhance clinical workflows, leading to more efficient and accurate diagnoses. AI advancements of medical imaging not only address existing unsolved problems but also present new and complex challenges. Solutions to these challenges can improve image quality and reveal new information currently obscured by noise, artifacts, or other signals. Holistic insight is the key to solving these challenges. Such insight may lead to a creative solution only when it is based on a thorough understanding of existing methods and unmet demands. This book focuses on advanced topics in medical imaging modalities, including CT and ultrasound, with the aim of providing practical applications in the healthcare industry. It strikes a balance between mathematical theory, numerical practice, and clinical applications, offering comprehensive coverage from basic to advanced levels of mathematical theories, deep learning techniques, and algorithm implementation details. Moreover, it provides in-depth insights into the latest advancements in dental cone-beam CT, fetal ultrasound, and bioimpedance, making it an essential resource for professionals seeking to stay up-to-date with the latest developments in the field of medical imaging.