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Anatomical Landmark Detection Leveraging Implicit and Explicit Information Sharing Techniques

Anatomical Landmark Detection Leveraging Implicit and Explicit Information Sharing Techniques
Author: Alexander Blair Powers
Publisher:
Total Pages: 0
Release: 2021
Genre: Diagnostic imaging
ISBN:

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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.


Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016
Author: Sebastien Ourselin
Publisher: Springer
Total Pages: 666
Release: 2016-10-17
Genre: Computers
ISBN: 3319467263

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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.


Medical Image Recognition, Segmentation and Parsing

Medical Image Recognition, Segmentation and Parsing
Author: S. Kevin Zhou
Publisher: Academic Press
Total Pages: 548
Release: 2015-12-11
Genre: Computers
ISBN: 0128026766

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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


Vibrant and Healthy Kids

Vibrant and Healthy Kids
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 621
Release: 2019-12-27
Genre: Medical
ISBN: 0309493382

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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.


How Learning Works

How Learning Works
Author: Susan A. Ambrose
Publisher: John Wiley & Sons
Total Pages: 336
Release: 2010-04-16
Genre: Education
ISBN: 0470617608

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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


Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Author: Anne L. Martel
Publisher: Springer Nature
Total Pages: 867
Release: 2020-10-02
Genre: Computers
ISBN: 3030597199

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The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography


Automated Machine Learning

Automated Machine Learning
Author: Frank Hutter
Publisher: Springer
Total Pages: 223
Release: 2019-05-17
Genre: Computers
ISBN: 3030053180

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This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.


Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

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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.


Autonomous Horizons

Autonomous Horizons
Author: Greg Zacharias
Publisher: Independently Published
Total Pages: 420
Release: 2019-04-05
Genre:
ISBN: 9781092834346

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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.


Hands

Hands
Author: Ulf Grenander
Publisher: Springer Science & Business Media
Total Pages: 164
Release: 2012-12-06
Genre: Computers
ISBN: 1461230462

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In this book a global shape model is developed and applied to the analysis of real pictures acquired with a visible light camera under varying conditions of optical degradation. Computational feasibility of the algorithms derived from this model is achieved by analytical means. The aim is to develop methods for image understanding based on structured restoration, for example automatic detection of abnormalities. We also want to find the limits of applicability of the algorithms. This is done by making the optical degradations more and more severe until the algorithms no longer succeed in their task. This computer experiment in pattern theory is one of several. The others, LEAVES, X-RAYS, and RANGE are described elsewhere. This book is suitable for an advanced undergraduate or graduate seminar in pattern theory, or as an accompanying book for applied probability, computer vision, or pattern recognition.