Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author: Lia Morra,Silvia Delsanto,Loredana Correale
Publsiher: CRC Press
Total Pages: 152
Release: 2019-11-25
ISBN 10: 1000753085
ISBN 13: 9781000753080
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Medical Imaging Book Review:

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 in Medical Imaging

Machine Learning in Medical Imaging
Author: Kenji Suzuki,Fei Wang,Dinggang Shen,Pingkun Yan
Publsiher: Springer
Total Pages: 371
Release: 2011-09-25
ISBN 10: 3642243193
ISBN 13: 9783642243196
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Review:

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.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Chunfeng Lian,Xiaohuan Cao,Islem Rekik,Xuanang Xu,Pingkun Yan
Publsiher: Springer Nature
Total Pages: 704
Release: 2021-09-25
ISBN 10: 303087589X
ISBN 13: 9783030875893
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Review:

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

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures
Author: Hayit Greenspan,Ryutaro Tanno,Marius Erdt,Tal Arbel,Christian Baumgartner,Adrian Dalca,Carole H. Sudre,William M. Wells,Klaus Drechsler,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester
Publsiher: Springer Nature
Total Pages: 192
Release: 2019-10-10
ISBN 10: 3030326896
ISBN 13: 9783030326890
Language: EN, FR, DE, ES & NL

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

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.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Mingxia Liu,Pingkun Yan,Chunfeng Lian,Xiaohuan Cao
Publsiher: Springer Nature
Total Pages: 686
Release: 2020-10-02
ISBN 10: 3030598616
ISBN 13: 9783030598617
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Review:

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 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.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
Author: Farah Deeba,Patricia Johnson,Tobias Würfl,Jong Chul Ye
Publsiher: Springer Nature
Total Pages: 163
Release: 2020-10-21
ISBN 10: 3030615987
ISBN 13: 9783030615987
Language: EN, FR, DE, ES & NL

Machine Learning for Medical Image Reconstruction Book Review:

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.

Medical Imaging

Medical Imaging
Author: K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publsiher: CRC Press
Total Pages: 238
Release: 2019-08-20
ISBN 10: 0429642490
ISBN 13: 9780429642494
Language: EN, FR, DE, ES & NL

Medical Imaging Book Review:

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 Models for Medical Imaging

Deep Learning Models for Medical Imaging
Author: K.C. Santosh,Nibaran Das,Swarnendu Ghosh
Publsiher: Academic Press
Total Pages: 170
Release: 2021-09-17
ISBN 10: 0128236507
ISBN 13: 9780128236505
Language: EN, FR, DE, ES & NL

Deep Learning Models for Medical Imaging Book Review:

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

Deep Learning in Medical Image Analysis
Author: Gobert Lee,Hiroshi Fujita
Publsiher: Springer Nature
Total Pages: 181
Release: 2020-02-06
ISBN 10: 3030331288
ISBN 13: 9783030331283
Language: EN, FR, DE, ES & NL

Deep Learning in Medical Image Analysis Book Review:

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.

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author: Erik R. Ranschaert,Sergey Morozov,Paul R. Algra
Publsiher: Springer
Total Pages: 373
Release: 2019-01-29
ISBN 10: 3319948784
ISBN 13: 9783319948782
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Medical Imaging Book Review:

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.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author: S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publsiher: Academic Press
Total Pages: 458
Release: 2017-01-18
ISBN 10: 0128104090
ISBN 13: 9780128104095
Language: EN, FR, DE, ES & NL

Deep Learning for Medical Image Analysis Book Review:

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
Author: Guorong Wu,Daoqiang Zhang,Luping Zhou
Publsiher: Springer
Total Pages: 332
Release: 2014-09-05
ISBN 10: 3319105817
ISBN 13: 9783319105819
Language: EN, FR, DE, ES & NL

Machine Learning in Medical Imaging Book Review:

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author: Guorong Wu,Dinggang Shen,Mert Sabuncu
Publsiher: Academic Press
Total Pages: 512
Release: 2016-08-11
ISBN 10: 0128041145
ISBN 13: 9780128041147
Language: EN, FR, DE, ES & NL

Machine Learning and Medical Imaging Book Review:

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
Author: Archana Mire,Vinayak Elangovan,Shailaja Patil
Publsiher: CRC Press
Total Pages: 168
Release: 2022-04-28
ISBN 10: 1000575950
ISBN 13: 9781000575958
Language: EN, FR, DE, ES & NL

Advances in Deep Learning for Medical Image Analysis Book Review:

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.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Graphs in Biomedical Image Analysis

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging  and Graphs in Biomedical Image Analysis
Author: Carole H. Sudre,Hamid Fehri,Tal Arbel,Christian F. Baumgartner,Adrian Dalca,Ryutaro Tanno,Koen Van Leemput,William M. Wells,Aristeidis Sotiras,Bartlomiej Papiez,Enzo Ferrante,Sarah Parisot
Publsiher: Springer Nature
Total Pages: 233
Release: 2020-10-05
ISBN 10: 3030603652
ISBN 13: 9783030603656
Language: EN, FR, DE, ES & NL

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Graphs in Biomedical Image Analysis Book Review:

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 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. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
Author: Nandinee Haq,Patricia Johnson,Andreas Maier,Tobias Würfl,Jaejun Yoo
Publsiher: Springer Nature
Total Pages: 142
Release: 2021-09-29
ISBN 10: 3030885526
ISBN 13: 9783030885526
Language: EN, FR, DE, ES & NL

Machine Learning for Medical Image Reconstruction Book Review:

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Deep Learning Applications in Medical Imaging

Deep Learning Applications in Medical Imaging
Author: Saxena, Sanjay,Paul, Sudip
Publsiher: IGI Global
Total Pages: 274
Release: 2020-10-16
ISBN 10: 1799850722
ISBN 13: 9781799850724
Language: EN, FR, DE, ES & NL

Deep Learning Applications in Medical Imaging Book Review:

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.

Artificial Intelligence and Machine Learning in 2D 3D Medical Image Processing

Artificial Intelligence and Machine Learning in 2D 3D Medical Image Processing
Author: Rohit Raja,Sandeep Kumar,Shilpa Rani,K. Ramya Laxmi
Publsiher: CRC Press
Total Pages: 196
Release: 2020-12-23
ISBN 10: 1000337138
ISBN 13: 9781000337136
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning in 2D 3D Medical Image Processing Book Review:

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field

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

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Author: M. Jorge Cardoso,Tal Arbel,Gustavo Carneiro,Tanveer Syeda-Mahmood,João Manuel R.S. Tavares,Mehdi Moradi,Andrew Bradley,Hayit Greenspan,João Paulo Papa,Anant Madabhushi,Jacinto C. Nascimento,Jaime S. Cardoso,Vasileios Belagiannis,Zhi Lu
Publsiher: Springer
Total Pages: 385
Release: 2017-09-07
ISBN 10: 3319675583
ISBN 13: 9783319675589
Language: EN, FR, DE, ES & NL

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

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. 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.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing
Author: Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publsiher: Springer
Total Pages: 326
Release: 2017-07-12
ISBN 10: 331942999X
ISBN 13: 9783319429991
Language: EN, FR, DE, ES & NL

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book Review:

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.