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This book contains the full papers presented at ICCEBS 2013 – the 1st International Conference on Computational and Experimental Biomedical Sciences, which was organized in Azores, in October 2013. The included papers present and discuss new trends in those fields, using several methods and techniques, including active shape models, constitutive models, isogeometric elements, genetic algorithms, level sets, material models, neural networks, optimization and the finite element method, in order to address more efficiently different and timely applications involving biofluids, computer simulation, computational biomechanics, image based diagnosis, image processing and analysis, image segmentation, image registration, scaffolds, simulation and surgical planning. The main audience for this book consists of researchers, Ph.D students and graduate students with multidisciplinary interests related to the areas of artificial intelligence, bioengineering, biology, biomechanics, computational fluid dynamics, computational mechanics, computational vision, histology, human motion, imagiology, applied mathematics, medical image, medicine, orthopaedics, rehabilitation, speech production and tissue engineering.
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Imaging mass spectrometry (MS) techniques are often utilized without an understanding of their underlying principles, making it difficult for scientists to determine when and how they can exploit MS to visualize their biomolecules of interest. Introduction to Spatial Mapping of Biomolecules by Imaging Mass Spectrometry is an essential reference to help scientists determine the status and strategies of biomolecule analysis, describing its many applications for diverse classes of biomolecules. The book builds a foundation of imaging MS knowledge by introducing ionization sources, sample preparation, visualization guidelines, molecule identification, quantification, data analysis, etc. The second section contains chapters focused on case studies on analyzing a biomolecule class of molecules. Case studies include an introduction/background, and a summary of successful imaging MS studies with illustrative figures and future directions. - Provides the introductory foundations of imaging mass spectrometry for those new to the technique - Organized by topic to facilitate a quick deep dive, allowing researchers to immediately apply the imaging MS techniques to their work - Includes case studies summarizing the imaging MS techniques developed for the class of molecules
This book focuses on the challenges and the recent findings in vision intelligence incorporating high performance computing applications. The contents provide in-depth discussions on a range of emerging multidisciplinary topics like computer vision, image processing, artificial intelligence, machine learning, cloud computing, IoT, and big data. The book also includes illustrations of algorithms, architecture, applications, software systems, and data analytics within the scope of the discussed topics. This book will help students, researchers, and technology professionals discover latest trends in the fields of computer vision and artificial intelligence.
The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
The two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.
This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images.
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
The huge volume of multi-modal neuroimaging data across different neuroscience communities has posed a daunting challenge to traditional methods of data sharing, data archiving, data processing and data analysis. Neuroinformatics plays a crucial role in creating advanced methodologies and tools for the handling of varied and heterogeneous datasets in order to better understand the structure and function of the brain. These tools and methodologies not only enhance data collection, analysis, integration, interpretation, modeling, and dissemination of data, but also promote data sharing and collaboration. This Neuroinformatics Research Topic aims to summarize the state-of-art of the current achievements and explores the directions for the future generation of neuroinformatics infrastructure. The publications present solutions for data archiving, data processing and workflow, data mining, and system integration methodologies. Some of the systems presented are large in scale, geographically distributed, and already have a well-established user community. Some discuss opportunities and methodologies that facilitate large-scale parallel data processing tasks under a heterogeneous computational environment. We wish to stimulate on-going discussions at the level of the neuroinformatics infrastructure including the common challenges, new technologies of maximum benefit, key features of next generation infrastructure, etc. We have asked leading research groups from different research areas of neuroscience/neuroimaging to provide their thoughts on the development of a state of the art and highly-efficient neuroinformatics infrastructure. Such discussions will inspire and help guide the development of a state of the art, highly-efficient neuroinformatics infrastructure.
The two-volume set LNCS 8258 and 8259 constitutes the refereed proceedings of the 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013, held in Havana, Cuba, in November 2013. The 137 papers presented, together with two keynotes, were carefully reviewed and selected from 262 submissions. The papers are organized in topical sections on mathematical theory of PR, supervised and unsupervised classification, feature or instance selection for classification, image analysis and retrieval, signals analysis and processing, applications of pattern recognition, biometrics, video analysis, and data mining.