Anatomical Landmark Detection Leveraging Implicit And Explicit Information Sharing Techniques
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| Author | : Alexander Blair Powers |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Diagnostic imaging |
| ISBN | : |
Anatomical landmark detection is an essential step in various medical imaging processes, including morphological analysis, inter-/intra-subject registration, and, fundamentally, anatomy orientation. Deep reinforcement learning (DRL) has shown promise in replacing heuristic methods and classical image processing approaches to landmark detection. In this work, we propose multiple extensions of a multi-agent deep q-network approach to anatomical landmark detection. We first improve the localization of high confidence primary landmarks by searching in the physical space coordinate system of the image rather than voxel space. Second, when detecting a large number of landmarks, we decompose the detection process into two stages to compensate for the memory limitations induced by detecting a large number of landmarks.
| Author | : Sebastien Ourselin |
| Publisher | : Springer |
| Total Pages | : 666 |
| Release | : 2016-10-17 |
| Genre | : Computers |
| ISBN | : 3319467263 |
The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 621 |
| Release | : 2019-12-27 |
| Genre | : Medical |
| ISBN | : 0309493382 |
Children are the foundation of the United States, and supporting them is a key component of building a successful future. However, millions of children face health inequities that compromise their development, well-being, and long-term outcomes, despite substantial scientific evidence about how those adversities contribute to poor health. Advancements in neurobiological and socio-behavioral science show that critical biological systems develop in the prenatal through early childhood periods, and neurobiological development is extremely responsive to environmental influences during these stages. Consequently, social, economic, cultural, and environmental factors significantly affect a child's health ecosystem and ability to thrive throughout adulthood. Vibrant and Healthy Kids: Aligning Science, Practice, and Policy to Advance Health Equity builds upon and updates research from Communities in Action: Pathways to Health Equity (2017) and From Neurons to Neighborhoods: The Science of Early Childhood Development (2000). This report provides a brief overview of stressors that affect childhood development and health, a framework for applying current brain and development science to the real world, a roadmap for implementing tailored interventions, and recommendations about improving systems to better align with our understanding of the significant impact of health equity.
| Author | : Susan A. Ambrose |
| Publisher | : John Wiley & Sons |
| Total Pages | : 336 |
| Release | : 2010-04-16 |
| Genre | : Education |
| ISBN | : 0470617608 |
Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning
| Author | : S. Kevin Zhou |
| Publisher | : Academic Press |
| Total Pages | : 548 |
| Release | : 2015-12-11 |
| Genre | : Computers |
| ISBN | : 0128026766 |
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: - Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects - Methods and theories for medical image recognition, segmentation and parsing of multiple objects - Efficient and effective machine learning solutions based on big datasets - Selected applications of medical image parsing using proven algorithms - Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects - Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets - Includes algorithms for recognizing and parsing of known anatomies for practical applications
| Author | : Shaoshan Liu |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 285 |
| Release | : 2017-10-25 |
| Genre | : Computers |
| ISBN | : 1681731673 |
This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
| Author | : Christopher M. Bishop |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2016-08-23 |
| Genre | : Computers |
| ISBN | : 9781493938438 |
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
| Author | : Julian McAuley |
| Publisher | : Cambridge University Press |
| Total Pages | : 338 |
| Release | : 2022-02-03 |
| Genre | : Computers |
| ISBN | : 1009008579 |
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
| Author | : Greg Zacharias |
| Publisher | : Independently Published |
| Total Pages | : 420 |
| Release | : 2019-04-05 |
| Genre | : |
| ISBN | : 9781092834346 |
Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.
| Author | : Marc Peter Deisenroth |
| Publisher | : Cambridge University Press |
| Total Pages | : 392 |
| Release | : 2020-04-23 |
| Genre | : Computers |
| ISBN | : 1108569323 |
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
