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Author: Jyotismita Chaki Publisher: Academic Press ISBN: 0323983952 Category : Science Languages : en Pages : 260
Book Description
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. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Author: Jyotismita Chaki Publisher: Academic Press ISBN: 0323983952 Category : Science Languages : en Pages : 260
Book Description
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. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Author: Jyotismita Chaki Publisher: Elsevier ISBN: 0323911714 Category : Science Languages : en Pages : 258
Book Description
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. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Author: Gustavo Carneiro Publisher: Springer ISBN: 3319469762 Category : Computers Languages : en Pages : 280
Book Description
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Author: Annamalai Suresh Publisher: Medical Information Science Reference ISBN: 9781799835936 Category : Computers Languages : en Pages : 294
Book Description
The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.
Author: Fatih ÖZYURT Publisher: Infinite Study ISBN: Category : Mathematics Languages : en Pages : 16
Book Description
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Author: Antonios Drevelegas Publisher: Springer Science & Business Media ISBN: 3540876502 Category : Medical Languages : en Pages : 434
Book Description
This volume provides a deeper understanding of the diagnosis of brain tumors by correlating radiographic imaging features with the underlying pathological abnormalities. All modern imaging modalities are used to complete a diagnostic overview of brain tumors with emphasis on recent advances in diagnostic neuroradiology. High-quality illustrations depicting common and uncommon imaging characteristics of a wide range of brain tumors are presented and analysed, drawing attention to the ways in which these characteristics reflect different aspects of pathology. Important theoretical considerations are also discussed. Since the first edition, chapters have been revised and updated and new material has been added, including detailed information on the clinical application of functional MRI and diffusion tensor imaging. Radiologists and other clinicians interested in the current diagnostic approach to brain tumors will find this book to be an invaluable and enlightening clinical tool.
Author: Ben Othman Soufiene Publisher: CRC Press ISBN: 1003805671 Category : Technology & Engineering Languages : en Pages : 270
Book Description
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of 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 and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Author: Miral D. Jhaveri Publisher: Elsevier Health Sciences ISBN: 0323400434 Category : Medical Languages : en Pages : 1237
Book Description
More than 300 diagnoses that are delineated, referenced, and lavishly illustrated highlight the third edition of this bestselling reference. World-renowned authority Dr. Anne G. Osborn and her expert author team of Drs. Karen L. Salzman and Miral D. Jhaveri provide carefully updated information in a concise, bulleted format, keeping you current with new disease entities and syndromes, MR imaging techniques and applications, and pathology relevant to brain imaging. Succinct text, outstanding illustrations, and up-to-date content make this title a must-have reference for neuroradiologists, general radiologists, neurologists, and neurosurgeons. Concise, bulleted text provides efficient information on more than 300 diagnoses that are clearly illustrated with 2,500 superb images Meticulously updated throughout, with new diagnoses and hundreds of new images that provide the most current information in the field. Expert guidance on CLIPPERS, second-impact syndrome in trauma, perfusion MR for tumor characterization, susceptibility-weighted imaging in stroke and brain bleeds, and molecular markers in brain tumor classification and grading. Updated coverage of brain trauma addresses newly recognized entities, techniques and imaging for rapid stroke triage, and functional imaging and dementia diagnosis.
Author: Jyotismita Chaki
Author: Jyotismita Chaki
Author: Jyotismita Chaki
Author: Gustavo Carneiro
Author: Annamalai Suresh
Author: Fatih ÖZYURT
Author: Antonios Drevelegas
Author: Dr. Sasidhar B
Author: Bjoern Menze
Author: Ben Othman Soufiene
Author: Miral D. Jhaveri