Deep Learning For Biomedical Applications
DOWNLOAD
Download Deep Learning For Biomedical Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning For Biomedical Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Machine Learning For Biomedical Applications
DOWNLOAD
Author : Maria Deprez
language : en
Publisher: Academic Press
Release Date : 2023-09-07
Machine Learning For Biomedical Applications written by Maria Deprez and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-07 with Computers categories.
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians. - Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis. - Shows how to apply a range of commonly used machine learning and deep learning techniques to biomedical problems. - Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets. - Shows how to design machine learning experiments that address specific problems related to biomedical data
Deep Learning For Biomedical Applications
DOWNLOAD
Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2021-07-19
Deep Learning For Biomedical Applications written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Technology & Engineering categories.
This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.
Handbook Of Deep Learning In Biomedical Engineering
DOWNLOAD
Author : Valentina Emilia Balas
language : en
Publisher: Academic Press
Release Date : 2020-11-12
Handbook Of Deep Learning In Biomedical Engineering written by Valentina Emilia Balas and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-12 with Science categories.
Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. - Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT - Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis - Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks - Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer's, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography
Deep Learning In Biomedical Signal And Medical Imaging
DOWNLOAD
Author : Ngangbam Herojit Singh
language : en
Publisher: CRC Press
Release Date : 2024-09-30
Deep Learning In Biomedical Signal And Medical Imaging written by Ngangbam Herojit Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives. Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer’s, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader’s head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
DOWNLOAD
Author : Shadi N. M. Albarqouni
language : en
Publisher:
Release Date : 2017
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning written by Shadi N. M. Albarqouni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Deep Learning And Computer Vision Models And Biomedical Applications
DOWNLOAD
Author : Uma N. Dulhare
language : en
Publisher: Springer Nature
Release Date : 2025-07-18
Deep Learning And Computer Vision Models And Biomedical Applications written by Uma N. Dulhare and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Computers categories.
This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Deep Learning And Computer Vision Models And Biomedical Applications
DOWNLOAD
Author : Uma N. Dulhare
language : en
Publisher: Springer Nature
Release Date : 2025-03-08
Deep Learning And Computer Vision Models And Biomedical Applications written by Uma N. Dulhare and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-08 with Computers categories.
This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
DOWNLOAD
Author : Shadi Albarqouni
language : en
Publisher:
Release Date : 2017
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning written by Shadi Albarqouni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Deep Learning For Medical Applications With Unique Data
DOWNLOAD
Author : Deepak Gupta
language : en
Publisher: Academic Press
Release Date : 2022-02-15
Deep Learning For Medical Applications With Unique Data written by Deepak Gupta and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-15 with Science categories.
Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. - Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets - Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis - Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
Deep Neural Networks For Multimodal Imaging And Biomedical Applications
DOWNLOAD
Author : Suresh, Annamalai
language : en
Publisher: IGI Global
Release Date : 2020-06-26
Deep Neural Networks For Multimodal Imaging And Biomedical Applications written by Suresh, Annamalai and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-26 with Computers categories.
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.