Products related to Segmentation:
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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques
Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors.The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation.After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties.A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation.
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Deep Learning Applications in Medical Image Segmentation : Overview, Approaches, and Challenges
Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment.The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields to impact medical image segmentation, is immense.The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge.Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction to this technology and its growing applications.Covering both the foundational concepts of the technology and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic.It's deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation.Readers will also find: Analysis of different deep learning models, including FCN, UNet, SegNet, Dee Lab, and many moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.
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Online Education Using Learning Objects
'E-learning is integral to on-site education institutions worldwide, and the rapid explosion of interest in the subject means that this timely, cutting-edge book will be an instant and indispensable resource.Among educators, the development of reusable learning objects made accessible via the internet is ever more important to teaching and learning.This book provides a comprehensive look at a state-of-the-art online education, and presents advice on the creation, adaptation and implementation of learning objects and metadata.Including articles written by some of the leading innovators in the field, this book takes the reader through: designing effective learning objects; creating learning objects; transforming existing content into reusable learning objects; building a metadata management system.This book will be essential reference material for learning technologists, course developers at learning institutions, postgraduate students, teachers and learners in the field of e-learning.'
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Distance Learning and Online Education
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What is market segmentation?
Market segmentation is the process of dividing a broad target market into smaller, more defined segments based on certain characteristics such as demographics, psychographics, behavior, or geographic location. This allows businesses to tailor their products, services, and marketing strategies to better meet the specific needs and preferences of each segment. By understanding the unique characteristics and preferences of different segments, businesses can more effectively reach and engage with their target customers.
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What is the difference between fragmentation and segmentation?
Fragmentation refers to the process of breaking a file or data into smaller pieces to fit into the available storage space. This can lead to scattered pieces of data across the storage medium, which can slow down access times. On the other hand, segmentation is the process of dividing the memory into segments to store different parts of a program or data. Segmentation allows for more efficient memory management and can help prevent fragmentation. In summary, fragmentation deals with the physical storage of data, while segmentation deals with the logical organization of memory.
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What is the purpose of market segmentation strategy?
The purpose of market segmentation strategy is to divide a broad target market into smaller, more manageable segments based on similar characteristics, needs, or behaviors. This allows businesses to better understand and cater to the specific needs and preferences of different customer groups, leading to more effective marketing and product development. By tailoring their offerings to specific segments, businesses can increase customer satisfaction, loyalty, and ultimately, profitability. Additionally, market segmentation helps businesses identify new opportunities for growth and expansion within different market segments.
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What is the difference between market segmentation and strategic business unit?
Market segmentation is the process of dividing a market into distinct groups of buyers who have different needs, characteristics, or behaviors. This allows businesses to tailor their marketing efforts and products to specific segments, increasing their effectiveness. On the other hand, a strategic business unit (SBU) is a distinct business or product line within a larger company that has its own set of competitors and a specific target market. SBUs are often managed separately from the rest of the company and have their own strategic objectives and business plans. In summary, market segmentation is about dividing the overall market into smaller, more manageable segments, while an SBU is a distinct business entity within a larger organization.
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Automatic Dough Dividing Machine Commercial Pizza Bread bBll Segmentation
Automatic Dough Dividing Machine Commercial Pizza Bread bBll Segmentation
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Auto-Segmentation for Radiation Oncology : State of the Art
This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning.Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications.Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations).This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use.Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine
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Desktop Dough Circular Machine Multifunctional Commercial Dough Segmentation Machine
Desktop Dough Circular Machine Multifunctional Commercial Dough Segmentation Machine
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Desktop Dough Circular Machine Multifunctional Commercial Dough Segmentation Machine
Desktop Dough Circular Machine Multifunctional Commercial Dough Segmentation Machine
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Why don't the children use the online learning resources?
There could be several reasons why the children are not using online learning resources. Some possible reasons could include lack of access to technology or internet connectivity, difficulty navigating the online platforms, lack of motivation or engagement with the material, or preference for traditional in-person learning methods. It is important to understand the specific barriers that are preventing the children from using online resources in order to address them effectively and promote their learning.
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What should one do when a Segmentation fault (core dumped) error occurs in Python?
When a Segmentation fault (core dumped) error occurs in Python, it usually indicates a serious issue with the program, such as accessing memory that is not allocated or trying to perform an operation that is not allowed. To address this error, one should carefully review the code for any potential memory-related issues, such as accessing out-of-bounds memory or using uninitialized variables. Additionally, running the program with a debugger can help pinpoint the exact location of the error and provide more information on what is causing it. Finally, updating Python and any relevant libraries to the latest versions may also help resolve the issue.
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Why do I get a Segmentation Fault Error when running the program with an additional argument?
A Segmentation Fault Error occurs when a program tries to access a memory location that it is not allowed to access. This can happen when an additional argument is passed to the program that is not properly handled or expected by the code. The program may try to access memory beyond its allocated space, leading to the Segmentation Fault Error. To resolve this issue, ensure that the program is properly designed to handle additional arguments or input.
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How do I write a program for prime numbers that causes a segmentation fault and generates a core dump?
To write a program for prime numbers that causes a segmentation fault and generates a core dump, you can intentionally create a bug in your code. One way to do this is by accessing memory that is not allocated to your program, such as by dereferencing a null pointer or accessing an array out of bounds. For example, you can create a function to check for prime numbers and intentionally introduce a bug that causes a segmentation fault, such as by accessing an array element beyond its bounds. When the program is run, it will crash with a segmentation fault and generate a core dump, which can be analyzed to identify the cause of the error.
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