Deep Learning Models for Medical Imaging Book

Deep Learning Models for Medical Imaging


  • Author : K.C. Santosh
  • Publisher : Academic Press
  • Release Date : 2021-09-17
  • Genre: Computers
  • Pages : 170
  • ISBN 10 : 9780128236505

GET BOOK
Deep Learning Models for Medical Imaging Excerpt :

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation (source codes: available upon request)

Deep Learning in Medical Image Analysis Book

Deep Learning in Medical Image Analysis


  • Author : Gobert Lee
  • Publisher : Springer Nature
  • Release Date : 2020-02-06
  • Genre: Medical
  • Pages : 181
  • ISBN 10 : 9783030331283

GET BOOK
Deep Learning in Medical Image Analysis Excerpt :

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.

Machine Learning in Medical Imaging Book

Machine Learning in Medical Imaging


  • Author : Kenji Suzuki
  • Publisher : Springer
  • Release Date : 2011-09-25
  • Genre: Computers
  • Pages : 371
  • ISBN 10 : 9783642243196

GET BOOK
Machine Learning in Medical Imaging Excerpt :

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.

Advances in Deep Learning for Medical Image Analysis Book

Advances in Deep Learning for Medical Image Analysis


  • Author : Archana Mire
  • Publisher : CRC Press
  • Release Date : 2022-04-28
  • Genre: Technology & Engineering
  • Pages : 168
  • ISBN 10 : 9781000575958

GET BOOK
Advances in Deep Learning for Medical Image Analysis Excerpt :

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Deep Learning for Medical Image Analysis Book

Deep Learning for Medical Image Analysis


  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2017-01-18
  • Genre: Computers
  • Pages : 458
  • ISBN 10 : 9780128104095

GET BOOK
Deep Learning for Medical Image Analysis Excerpt :

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book

Deep Learning and Convolutional Neural Networks for Medical Image Computing


  • Author : Le Lu
  • Publisher : Springer
  • Release Date : 2017-07-12
  • Genre: Computers
  • Pages : 326
  • ISBN 10 : 9783319429991

GET BOOK
Deep Learning and Convolutional Neural Networks for Medical Image Computing Excerpt :

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.

Deep Neural Networks for Multimodal Imaging and Biomedical Applications Book

Deep Neural Networks for Multimodal Imaging and Biomedical Applications


  • Author : Suresh, Annamalai
  • Publisher : IGI Global
  • Release Date : 2020-06-26
  • Genre: Computers
  • Pages : 294
  • ISBN 10 : 9781799835929

GET BOOK
Deep Neural Networks for Multimodal Imaging and Biomedical Applications Excerpt :

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.

Deep Learning Applications in Medical Imaging Book

Deep Learning Applications in Medical Imaging


  • Author : Sanjay Saxena
  • Publisher : Unknown
  • Release Date : 2020-08
  • Genre: Medical
  • Pages : 304
  • ISBN 10 : 1799850714

GET BOOK
Deep Learning Applications in Medical Imaging Excerpt :

Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

Medical Imaging Book

Medical Imaging


  • Author : K.C. Santosh
  • Publisher : CRC Press
  • Release Date : 2019-08-20
  • Genre: Computers
  • Pages : 238
  • ISBN 10 : 9780429642494

GET BOOK
Medical Imaging Excerpt :

The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support Book

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support


  • Author : Danail Stoyanov
  • Publisher : Springer
  • Release Date : 2018-09-19
  • Genre: Computers
  • Pages : 387
  • ISBN 10 : 9783030008895

GET BOOK
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support Excerpt :

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Machine Learning in Medical Imaging Book

Machine Learning in Medical Imaging


  • Author : Chunfeng Lian
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • Genre: Computers
  • Pages : 704
  • ISBN 10 : 9783030875893

GET BOOK
Machine Learning in Medical Imaging Excerpt :

This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures Book

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures


  • Author : Hayit Greenspan
  • Publisher : Springer Nature
  • Release Date : 2019-10-10
  • Genre: Computers
  • Pages : 192
  • ISBN 10 : 9783030326890

GET BOOK
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures Excerpt :

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Artificial Intelligence in Medical Imaging Book

Artificial Intelligence in Medical Imaging


  • Author : Lia Morra
  • Publisher : CRC Press
  • Release Date : 2019-11-25
  • Genre: Science
  • Pages : 152
  • ISBN 10 : 9781000753080

GET BOOK
Artificial Intelligence in Medical Imaging Excerpt :

This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective

Machine Learning for Medical Image Reconstruction Book

Machine Learning for Medical Image Reconstruction


  • Author : Farah Deeba
  • Publisher : Springer Nature
  • Release Date : 2020-10-21
  • Genre: Computers
  • Pages : 163
  • ISBN 10 : 9783030615987

GET BOOK
Machine Learning for Medical Image Reconstruction Excerpt :

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Artificial Intelligence in Medical Imaging Book

Artificial Intelligence in Medical Imaging


  • Author : Erik R. Ranschaert
  • Publisher : Springer
  • Release Date : 2019-01-29
  • Genre: Medical
  • Pages : 373
  • ISBN 10 : 9783319948782

GET BOOK
Artificial Intelligence in Medical Imaging Excerpt :

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.