Comparative Analysis Of Deep Learning And Graph Cut Algorithms For Cell Image Segmentation
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| Author | : William L. William L. Hamilton |
| Publisher | : Springer Nature |
| Total Pages | : 141 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031015886 |
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
| Author | : Stegmaier, Johannes |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 264 |
| Release | : 2017-02-08 |
| Genre | : Electronic computers. Computer science |
| ISBN | : 3731505908 |
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.
| Author | : Yves Lechevallier |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 627 |
| Release | : 2010-11-08 |
| Genre | : Computers |
| ISBN | : 3790826049 |
Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications.
| Author | : Xizhao Wang |
| Publisher | : Springer |
| Total Pages | : 460 |
| Release | : 2014-12-04 |
| Genre | : Computers |
| ISBN | : 3662456524 |
This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.
| Author | : Gobert Lee |
| Publisher | : Springer Nature |
| Total Pages | : 184 |
| Release | : 2020-02-06 |
| Genre | : Medical |
| ISBN | : 3030331288 |
This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
| Author | : Valliappa Lakshmanan |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 481 |
| Release | : 2021-07-21 |
| Genre | : Computers |
| ISBN | : 1098102339 |
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
| Author | : Thompson Stephan |
| Publisher | : CRC Press |
| Total Pages | : 265 |
| Release | : 2024-07-18 |
| Genre | : Computers |
| ISBN | : 1040037445 |
In the ever-evolving realm of healthcare, Artificial Intelligence in Medicine emerges as a trailblazing guide, offering an extensive exploration of the transformative power of Artificial Intelligence (AI). Crafted by leading experts in the field, this book sets out to bridge the gap between theoretical understanding and practical application, presenting a comprehensive journey through the foundational principles, cutting-edge applications, and the potential impact of AI in the medical landscape. This book embarks on a journey from foundational principles to advanced applications, presenting a holistic perspective on the integration of AI into diverse aspects of medicine. With a clear aim to cater to both researchers and practitioners, the scope extends from fundamental AI techniques to their innovative applications in disease detection, prediction, and patient care. Distinguished by its practical orientation, each chapter presents actionable workflows, making theoretical concepts directly applicable to real-world medical scenarios. This unique approach sets the book apart, making it an invaluable resource for learners and practitioners alike. Key Features: • Comprehensive Exploration: From deep learning approaches for cardiac arrhythmia to advanced algorithms for ocular disease detection, the book provides an in-depth exploration of critical topics, ensuring a thorough understanding of AI in medicine. • Cutting-Edge Applications: The book delves into cutting-edge applications, including a vision transformer-based approach for brain tumor detection, early diagnosis of skin cancer, and a deep learning-based model for early detection of COVID-19 using chest X-ray images. • Practical Insights: Practical workflows and demonstrations guide readers through the application of AI techniques in real-world medical scenarios, offering insights that transcend theoretical boundaries. This book caters to researchers, practitioners, and students in medicine, computer science, and healthcare technology. With a focus on practical applications, this book is an essential guide for navigating the dynamic intersection of AI and medicine. Whether you are an expert or a newcomer to the field, this comprehensive volume provides a roadmap to the revolutionary impact of AI on the future of healthcare.
| Author | : Meenu Gupta |
| Publisher | : CRC Press |
| Total Pages | : 202 |
| Release | : 2021-12-31 |
| Genre | : Computers |
| ISBN | : 1000508668 |
Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.
| Author | : Zhang, Yu-Jin |
| Publisher | : IGI Global |
| Total Pages | : 472 |
| Release | : 2006-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 1591407559 |
"This book attempts to bring together a selection of the latest results of state-of-the art research in image and video segmentation, one of the most critical tasks of image and video analysis that has the objective of extracting information (represented by data) from an image or a sequence of images (video)"--Provided by publisher.
| Author | : Zaman, Noor |
| Publisher | : IGI Global |
| Total Pages | : 293 |
| Release | : 2022-02-25 |
| Genre | : Computers |
| ISBN | : 1799889300 |
The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students.
