Machine Learning and Medical Imaging

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  • Publisher : Academic Press
  • Release : 11 August 2016
  • ISBN : 9780128041147
  • Page : 512 pages
  • Rating : 4.5/5 from 103 voters

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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

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Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
  • Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
  • Publisher : Academic Press
  • Release Date : 2016-08-11
  • ISBN : 9780128041147
GET THIS BOOKMachine Learning and Medical Imaging

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
  • Publisher : Springer
  • Release Date : 2019-10-10
  • ISBN : 3030326918
GET THIS BOOKMachine Learning in Medical Imaging

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the 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,

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
  • Author : Lia Morra,Silvia Delsanto,Loredana Correale
  • Publisher : CRC Press
  • Release Date : 2019-11-25
  • ISBN : 9781000753080
GET THIS BOOKArtificial Intelligence in Medical Imaging

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Chunfeng Lian,Xiaohuan Cao,Islem Rekik,Xuanang Xu,Pingkun Yan
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • ISBN : 9783030875893
GET THIS BOOKMachine Learning in Medical Imaging

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

Medical Imaging

Medical Imaging
  • Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
  • Publisher : CRC Press
  • Release Date : 2019-08-20
  • ISBN : 9780429639326
GET THIS BOOKMedical Imaging

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Mingxia Liu,Pingkun Yan,Chunfeng Lian,Xiaohuan Cao
  • Publisher : Springer Nature
  • Release Date : 2020-10-02
  • ISBN : 9783030598617
GET THIS BOOKMachine Learning in Medical Imaging

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

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
  • Publisher : Springer Nature
  • Release Date : 2019-10-10
  • ISBN : 9783030326890
GET THIS BOOKUncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

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

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
  • Author : Erik R. Ranschaert,Sergey Morozov,Paul R. Algra
  • Publisher : Springer
  • Release Date : 2019-01-29
  • ISBN : 9783319948782
GET THIS BOOKArtificial Intelligence in Medical Imaging

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

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
  • Publisher : Springer Nature
  • Release Date : 2020-10-05
  • ISBN : 9783030603656
GET THIS BOOKUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

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

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
  • Publisher : Academic Press
  • Release Date : 2017-01-18
  • ISBN : 9780128104095
GET THIS BOOKDeep Learning for Medical Image Analysis

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

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
  • Author : Gobert Lee,Hiroshi Fujita
  • Publisher : Springer Nature
  • Release Date : 2020-02-06
  • ISBN : 9783030331283
GET THIS BOOKDeep Learning in Medical Image Analysis

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

Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
  • Publisher : Academic Press
  • Release Date : 2021-09-17
  • ISBN : 9780128236505
GET THIS BOOKDeep Learning Models for Medical Imaging

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

Deep Learning Applications in Medical Imaging

Deep Learning Applications in Medical Imaging
  • Author : Saxena, Sanjay,Paul, Sudip
  • Publisher : IGI Global
  • Release Date : 2020-10-16
  • ISBN : 9781799850724
GET THIS BOOKDeep Learning Applications in Medical Imaging

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

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
  • Author : Archana Mire,Vinayak Elangovan,Shailaja Patil
  • Publisher : CRC Press
  • Release Date : 2022-04-28
  • ISBN : 9781000575958
GET THIS BOOKAdvances in Deep Learning for Medical Image Analysis

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,

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
  • Publisher : CRC Press
  • Release Date : 2020-12-22
  • ISBN : 9781000337075
GET THIS BOOKArtificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

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